The proliferation of facial recognition (FR) systems has raised privacy concerns in the digital realm, as malicious uses of FR models pose a significant threat. Traditional countermeasures, such as makeup style transfer, have suffered from low transferability in black-box settings and limited applicability across various demographic groups, including males and individuals with darker skin tones. To address these challenges, we introduce a novel facial privacy protection method, dubbed MAP, a pioneering approach that employs human emotion modifications to disguise original identities as target identities in facial images. Our method uniquely fine-tunes a score network to learn dual objectives, target identity and human expression, which are jointly optimized through gradient projection to ensure convergence at a shared local optimum. Additionally, we enhance the perceptual quality of protected images by applying local smoothness regularization and optimizing the score matching loss within our network. Empirical experiments demonstrate that our innovative approach surpasses previous baselines, including noise-based, makeup-based, and freeform attribute methods, in both qualitative fidelity and quantitative metrics. Furthermore, MAP proves its effectiveness against an online FR API and shows advanced adaptability in uncommon photographic scenarios.

Verifying social media content from active conflict zones requires rapid geolocation, source attribution, forensic analysis, and multi-platform verification—tasks that overwhelm individual analysts at scale. We present Multi-agent OSINT Swarm for Automated Information Verification (MOSAIV), a three-stage agentic swarm built on Large Language Models (LLMs) for automated multimedia news verification. MOSAIV operates in three sequential phases: (1) a Prime Agent use 50 labeled training samples via few-shot in-context learning to produce a shared context document and a reusable 7-step verification skill specification; (2) multiple parallel Verification Agents each process social media posts using the primed skill, performing live web searches, OSINT analysis, and structured report generation; and (3) a dedicated Localization Agent independently verifies GPS coordinates and produces bounding-box-annotated evidence images, dual-panel OpenStreetMap location cards, and live source evidence thumbnails, multiple evidence artifacts per run in total. We evaluated 10 conflict-zone validation cases provided by the MV2026 challenge. MOSAIV achieves 10/10 precise GPS coordinates, 100% forensic analysis coverage, and an average of 8.1 independent sources per report, demonstrating that few-shot priming and agent swarms are critical for high-quality automated OSINT verification. We further conduct the first systematic model-capability scaling study across six Claude variants, identifying GPS precision, forensic completeness, and propaganda context detection.

The widespread use of synthetic image generation models and the challenges associated with authenticity preservation have fueled the demand for robust watermarking methods to safeguard authenticity and protect the copyright of synthetic images. Existing watermarking methods embed. Invisible signatures in synthetic images often compromise image quality and remain susceptible to multiple watermark removal attacks, including reconstruction and forgery methods. To overcome this issue, we propose a novel watermarking approach, AEON, which seamlessly integrates the watermark into the latent diffusion process and ensures the watermark aligns with scene semantics in the final image. Unlike existing invisible in-diffusion watermarking and traditional hash-based methods, our approach adapts the neural synthesized hash-based watermark to the semantics of the generated image during the intermediate diffusion process instead of embedding traditional hashes with the initial noise. Our proposed approach a) modulates the noise sampling in each diffusion denoising iteration through a learnable watermark embedding, b) optimizes consistency, reconstruction, and similarity loss, enforcing local and global alignment between the watermark structure and the underlying image content, and c) generates a strong watermark by allowing late embedding of the watermark in the diffusion process. Empirical results demonstrate the effectiveness of the proposed approach in retaining quality and its robustness against cumulative adversarial attacks.

Machine unlearning seeks to remove the influence of selected training examples without retraining the model from scratch. Recent work has extended this goal to vision–language models, yet existing datasets are not suited for judging whether a sample's influence has truly been erased from learned image–text pairs. Current algorithms often introduce false information into sentences generated after unlearning, which compromises utility. We first establish three criteria that an image caption unlearning method should meet: Specificity Reduction, Identity Removal, and Performance Preservation. Guided by these criteria, we present CelebCaption, an image–text dataset of 15,000 photographs covering 150 well-known individuals, each linked to four captions that vary in detail (detailed versus summary) and in the presence of the subject's name. This design enables controlled, quantitative assessment of the proposed unlearning objectives. We benchmark several representative unlearning algorithms on CelebCaption using both caption quality scores and membership inference attack accuracy as quantitative unlearning metrics, and observe that current methods fail to achieve their privacy objectives. Our unlearning criteria and dataset provide a focused, reproducible testbed for advancing privacy-aware image captioning. The CelebCaption dataset is publicly available at https://github.com/DASH-Lab/CelebCaption.

Machine Unlearning (MU) aims to remove target training data from a trained model so that the removed data no longer influences the model's behavior, fulfilling "right to be forgotten" obligations under data privacy laws. Yet, we observe that researchers in this rapidly emerging field face challenges in analyzing and understanding the behavior of different MU methods, especially in terms of three fundamental principles in MU: accuracy, efficiency, and privacy. Consequently, they often rely on aggregate metrics and ad-hoc evaluations, making it difficult to accurately assess the trade-offs between methods. To fill this gap, we introduce a visual analytics system, Unlearning Comparator, designed to facilitate the systematic evaluation of MU methods. Our system supports two important tasks in the evaluation process: model comparison and attack simulation. First, it allows the user to compare the behaviors of two models, such as a model generated by a certain method and a retrained baseline, at class-, instance-, and layer-levels to better understand the changes made after unlearning. Second, our system simulates membership inference attacks (MIAs) to evaluate the privacy of a method, where an attacker attempts to determine whether specific data samples were part of the original training set. We evaluate our system through a case study visually analyzing prominent MU methods and demonstrate that it helps the user not only understand model behaviors but also gain insights that can inform the improvement of MU methods.

To address the potential risks of AI while supporting innovation and ensuring responsible adoption, there is an urgent need for clear governance frameworks grounded in human-centric values. It is imperative that AI systems operate in ways that are transparent, trustworthy, and ethically sound. Developing truly human-centric AI goes beyond technical innovation. It requires interdisciplinary collaboration and diverse perspectives. This workshop will explore key challenges and emerging solutions in the development of human-centric AI, with a focus on explainability, trustworthiness, fairness, and privacy. We welcome both theoretical contributions and practical case studies that demonstrate how human-centered principles are realized in real-world AI systems. The official workshop webpage is available at https://xai.kaist.ac.kr/Workshop/hcai2025/, which provides comprehensive information about the program.

Machine unlearning has emerged as a significant research topic in response to the increasing demands for data privacy and compliance with privacy regulations. The main challenge is to eliminate the influence of a specific subset of training data from a pretrained model while preserving the model's performance on the retain set without retraining the model from scratch. In this paper, we propose a novel efficient unlearning framework based on Optimal Transport, which can effectively work on both class-wise and instance-wise unlearning tasks. By analyzing and comparing the feature spaces of the original and retrained models, we formulate the unlearning problem as a distribution alignment task between the forget set and the retain set. We guide the feature distribution of the forget set, which initially forms distinct and structured patterns, to align with that of the retain set. Extensive experiments on three public benchmark datasets demonstrate its superior effectiveness compared to previous state-of-the-art methods.

Multi-step or hybrid deepfakes, created by sequentially applying different deepfake creation methods such as Face-Swapping, GAN-based generation, and Diffusion methods, can pose an emerging and unforseen technical challenge for detection models trained on single-step forgeries. While prior studies have mainly focused on detecting isolated single manipulation, little is known about the detection model behavior under such compositional, hybrid, and complex manipulation pipelines. In this work, we introduce \textbf{FakeChain}, a large-scale benchmark comprising 1-, 2-, and 3-Step forgeries synthesized using five state-of-the-art representative generators. Using this approach, we analyze detection performance and spectral properties across hybrid manipulation at different step, along with varying generator combinations and quality settings. Surprisingly, our findings reveal that detection performance highly depends on the final manipulation type, with F1-score dropping by up to \textbf{58.83\%} when it differs from training distribution. This clearly demonstrates that detectors rely on last-stage artifacts rather than cumulative manipulation traces, limiting generalization. Such findings highlight the need for detection models to explicitly consider manipulation history and sequences. Our results highlight the importance of benchmarks such as FakeChain, reflecting growing synthesis complexity and diversity in real-world scenarios. Our sample code is available here\footnote{https://github.com/minjihh/FakeChain}.

Open-vocabulary object detection (OVD) in remote sensing (RS) has shown remarkable generalization capabilities across diverse RS imagery through alignment between image and text embeddings. Such methods have further improved detection performance by incorporating additional scene-level context from both visual and textual domains. However, existing methods approximate scene context by simply averaging the text embeddings of the image's object labels, which is insufficient to capture the rich linguistic context present in RS scenes. To address this limitation, we propose a novel Feature-Aligned Scene Encoding (FASE), which constructs comprehensive scene representations through high-quality captions generated by a specialized vision-language model. Our Feature Alignment Module (FAM) creates a robust scene representation by fusing domain-specific caption embeddings with general text features through dual-branch fusion with gating and cross-attention. This resulting representation then facilitates the alignment with visual features. By utilizing enhanced scene encoding only during training, our method internalizes rich contextual knowledge without increasing inference complexity. Experiments on multiple benchmarks demonstrate significant improvements over state-of-the-art methods, validating the effectiveness of our approach for OVD in RS.

The rapid advancement of generative AI has enabled the mass production of photorealistic synthetic images, blurring the boundary between authentic and fabricated visual content. This challenge is particularly evident in deepfake scenarios involving facial manipulation, but also extends to broader AI-generated content (AIGC) cases involving fully synthesized scenes. As such content becomes increasingly difficult to distinguish from reality, the integrity of visual media is under threat. To address this issue, we propose a physically interpretable deepfake detection framework and demonstrate that defocus blur can serve as an effective forensic signal. Defocus blur is a depth-dependent optical phenomenon that naturally occurs in camera-captured images due to lens focus and scene geometry. In contrast, synthetic images often lack realistic depth-of-field (DoF) characteristics. To capture these discrepancies, we construct a defocus blur map and use it as a discriminative feature for detecting manipulated content. Unlike RGB textures or frequency-domain signals, defocus blur arises universally from optical imaging principles and encodes physical scene structure. This makes it a robust and generalizable forensic cue. Our approach is supported by three in-depth feature analyses, and experimental results confirm that defocus blur provides a reliable and interpretable cue for identifying synthetic images. We aim for our defocus-based detection pipeline and interpretability tools to contribute meaningfully to ongoing research in media forensics. The implementation is publicly available at: https://github.com/irissun9602/Defocus-Deepfake-Detection

For electric vehicles, the Adaptive Cruise Control (ACC) in Advanced Driver Assistance Systems (ADAS) is designed to assist braking based on driving conditions and user patterns. However, the driving data collected during development are limited and lack diversity, leading to late or aggressive braking. Moreover, it is necessary to effectively identify anomalies in braking patterns, which is critical for self-driving autonomous vehicles. We propose Graph Neural Controlled Differential Equation Normalizing Flow (GDFlow), which leverages Normalizing Flow (NF) with Neural Controlled Differential Equations (NCDE) to learn the distribution of normal driving patterns. Our approach captures spatio-temporal information from sensor data and accurately models continuous changes in driving patterns. Additionally, we introduce a quantile-based maximum likelihood objective to improve the likelihood estimate of normal data at the margin of the distribution. We validate GDFlow using real-world electric vehicle driving data that we collected from Hyundai IONIQ5 and GV80EV. Our model achieves state-of-the-art (SOTA) performance compared to nine baselines across four dataset configurations of different vehicle types and drivers. Furthermore, our model outperforms the latest anomaly detection methods across four time series benchmark datasets. Our approach demonstrates superior efficiency in inference time compared to existing methods.

Audio-visual deepfake detection methods demonstrate strong performance on academic datasets but fail significantly when applied to real-world. To address the shortcomings of previous approaches, we utilize landmarks dynamic information. First, we propose Landmark-based Distillation (LBD), motivated by I-JEPA's representation learning approach. LBD utilizes KL-divergence to align facial landmark predictions from visual and audio encoders, enforcing focus on geometric facial features rather than spurious background information. Second, we introduce Multimodal Temporal Information Alignment (MTIA), which employs contrastive learning to enhance temporal consistency between audio and visual representations. We conduct experiments on academic datasets and web-based deepfakes collected from diverse social media platforms, serving as real-world examples. Our proposed landmark-guided distillation framework achieves computational efficiency while improving multimodal video deepfake detection performance across a diverse range of deepfakes compared to existing methods. The code is available at https://github.com/Ckck12/Beyond-Masking.

Deepfakes have rapidly emerged as a serious threat to society due to their ease of creation and dissemination, triggering the accelerated development of detection technologies. However, many existing detectors rely on lab-generated datasets for validation, which may not prepare them for novel, real-world deepfakes. This paper extensively reviews and analyzes state-of-the-art deepfake detectors, evaluating them against several critical criteria. These criteria categorize detectors into 4 high-level groups and 13 fine-grained sub-groups, aligned with a unified conceptual framework we propose. This classification offers practical insights into the factors affecting detector efficacy. We evaluate the generalizability of 16 leading detectors across comprehensive attack scenarios, including black-box, white-box, and gray-box settings. Our systematized analysis and experiments provide a deeper understanding of deepfake detectors and their generalizability, paving the way for future research and the development of more proactive defenses against deepfakes.

In fingerprint biometric systems, fingerprint recognition traditionally focuses on identifying individuals based on the distinct fingerprints of different fingers, which is finger-specific identity recognition (FsIR). However, real-world applications often require recognizing the same individual using fingerprints from different fingers, which is finger-agnostic identity recognition (FaIR). The FaIR task has proven challenging due to the prevailing assumption in the biometric field that there is no correlation between an individual’s different fingerprints. To address this issue, we propose a novel system, IP-Fing, which can learn the human-level similarity across the fingers. By using a pretrained localization encoder to capture fingerprint landmarks and the ArcFace marginal logit function, our IP-Fing recognition system can match a fingerprint query to all fingerprints of the same person while distinguishing them from others. We assess our method using comprehensive tests on three fingerprint datasets: our private fingerprint dataset, KO-RFing, which only has one sample per finger available, the public fingerprint dataset, CASIA-v5, which has a few missing fingerprint samples for the task of finger-agnostic identity recognition (FaIR), and NIST SD302b as an auxiliary. IP-Fing achieves the best AUC with an average of 94.0409 across the three datasets, showing that our method is more effective in applying FaIR than conventional methods. Furthermore, IP-Fing demonstrates superior AUC with an average of 97.7779 across the datasets in the task of traditional finger-specific identity recognition (FsIR).

Watermarking embeds imperceptible patterns into images for authenticity verification. However, existing methods often lack robustness against various transformations primarily including distortions, image regeneration, and adversarial perturbation, creating real-world challenges. In this work, we introduce SpecGuard, a novel watermarking approach for robust and invisible image watermarking. Unlike prior approaches, we embed the message inside hidden convolution layers by converting from the spatial domain to the frequency domain using spectral projection of a higher frequency band that is decomposed by wavelet projection. Spectral projection employs Fast Fourier Transform approximation to transform spatial data into the frequency domain efficiently. In the encoding phase, a strength factor enhances resilience against diverse attacks, including adversarial, geometric, and regeneration-based distortions, ensuring the preservation of copyrighted information. Meanwhile, the decoder leverages Parseval's theorem to effectively learn and extract the watermark pattern, enabling accurate retrieval under challenging transformations. We evaluate the proposed SpecGuard based on the embedded watermark's invisibility, capacity, and robustness. Comprehensive experiments demonstrate the proposed SpecGuard outperforms the state-of-the-art models. To ensure reproducibility, the full code is released on \href{https://github.com/inzamamulDU/SpecGuard_ICCV_2025}{\textcolor{blue}{\textbf{GitHub}}}.

Diffusion models have shown to be strong representation learners, showcasing state-of-the-art performance across multiple domains. Aside from accelerated sampling, DDIM also enables the inversion of real images back to their latent codes. A direct inheriting application of this inversion operation is real image editing, where the inversion yields latent trajectories to be utilized during the synthesis of the edited image. Unfortunately, this practical tool has enabled malicious users to freely synthesize misinformative or deepfake contents with greater ease, which promotes the spread of unethical and abusive, as well as privacy-, and copyright-infringing contents. While defensive algorithms such as AdvDM and Photoguard have been shown to disrupt the diffusion process on these images, the misalignment between their objectives and the iterative denoising trajectory at test time results in weak disruptive performance.In this work, we present the DDIM Inversion Attack (DIA) that attacks the integrated DDIM trajectory path. Our results support the effective disruption, surpassing previous defensive methods across various editing methods. We believe that our frameworks and results can provide practical defense methods against the malicious use of AI for both the industry and the research community. Our code is available here: https://anonymous.4open.science/r/DIA-13419/.

The primary goal of deep metric learning is to construct a comprehensive embedding space that can effectively represent samples originating from both intra- and inter-classes. Although extensive prior work has explored diverse metric functions and innovative training strategies, much of this work relies on default training data. Consequently, the potential variations inherent within this data remain largely unexplored, constraining the model's robustness to unseen images. In this context, we introduce the Spherical Embedding Expansion (SEE ) method. SEE aims to uncover the latent semantic variations in training data. Especially, our method augments the embedding space with synthetic representations based on Max-Mahalanobis distribution (MMD) centers, which maximize the dispersion of these synthetic features without increasing computational costs.We evaluated the efficacy of SEE on four renowned standard benchmarks for the image retrieval task. The results demonstrate that SEE consistently enhances the performance of conventional methods when integrated with them, setting a new benchmark for deep metric learning performance across all settings.

The rapid development and prevalence of generative AI have made it easy for people to create high-quality deepfake images and videos, but their abuses have also increased exponentially. To mitigate potential social disruption, it is crucial to quickly detect the authenticity of each deepfake content hidden in a sea of information. While researchers have worked on developing deep learning-based methods, the deepfake datasets utilized in these studies are far from the real world in terms of their qualities; most popular deepfake datasets are human-distinguishable. To address this problem, we present a novel deepfake dataset, HiDF, a high-quality and human-indistinguishable deepfake dataset consisting of 62K images and 8K videos. HiDF is a meticulously curated dataset that includes diverse subjects that have undergone rigorous quality checks. A comparison of the quality between HiDF and existing deepfake datasets demonstrates that HiDF is human-indistinguishable. Hence, it can be a valuable benchmark dataset for deepfake detection tasks. Data and code (https://github.com/DSAIL-SKKU/HiDF) are publicly available for future deepfake detection research.

The increasing realism of content generated by GANs and diffusion models has made deepfake detection significantly more challenging. Existing approaches often focus solely on spatial or frequency-domain features, limiting their generalization to unseen manipulations. We propose the Spectral Cross-Attentional Network (SpecXNet), a dual-domain architecture for robust deepfake detection. The core \textbf{Dual-Domain Feature Coupler (DDFC)} decomposes features into a local spatial branch for capturing texture-level anomalies and a global spectral branch that employs Fast Fourier Transform to model periodic inconsistencies. This dual-domain formulation allows SpecXNet to jointly exploit localized detail and global structural coherence, which are critical for distinguishing authentic from manipulated images. We also introduce the \textbf{Dual Fourier Attention (DFA)} module, which dynamically fuses spatial and spectral features in a content-aware manner. Built atop a modified XceptionNet backbone, we embed the DDFC and DFA modules within a separable convolution block. Extensive experiments on multiple deepfake benchmarks show that SpecXNet achieves state-of-the-art accuracy, particularly under cross-dataset and unseen manipulation scenarios, while maintaining real-time feasibility. Our results highlight the effectiveness of unified spatial-spectral learning for robust and generalizable deepfake detection. To ensure reproducibility, we released the full code on \href{https://github.com/inzamamulDU/SpecXNet}{\textcolor{blue}{\textbf{GitHub}}}.

The success of diffusion models has enabled effortless, high-quality image modifications that precisely align with users' intentions, thereby raising concerns about their potential misuse by malicious actors. Previous studies have attempted to mitigate such misuse through adversarial attacks. However, these approaches heavily rely on image-level inconsistencies, which pose fundamental limitations in addressing the influence of textual prompts. In this paper, we propose PromptFlare, a novel adversarial protection method designed to protect images from malicious modifications facilitated by diffusion-based inpainting models. Our approach leverages the cross-attention mechanism to exploit the intrinsic properties of prompt embeddings. Specifically, we identify and target shared token of prompts that is invariant and semantically uninformative, injecting adversarial noise to suppress the sampling process. The injected noise acts as a cross-attention decoy, diverting the model's focus away from meaningful prompt-image alignments and thereby neutralizing the effect of prompt. Extensive experiments on the EditBench dataset demonstrate that our method achieves state-of-the-art performance across various metrics while significantly reducing computational overhead and GPU memory usage. These findings highlight PromptFlare as a robust and efficient protection against unauthorized image manipulations. The code is available at https://github.com/NAHOHYUN-SKKU/PromptFlare.

The proliferation of deepfake technologies poses urgent challenges and serious risks to digital integrity, particularly within critical sectors such as forensics, journalism, and the legal system. While existing detection systems have made significant progress in classification accuracy, they typically function as black-box models, offering limited transparency and minimal support for human reasoning. This hinders their usability in real-world decision-making contexts, especially for non-expert users. We present DF-P2E (Deepfake: Prediction to Explanation), a novel multimodal framework that integrates visual, semantic, and narrative layers of explanation to make deepfake detection interpretable and accessible. The framework consists of three modular components: (1) a deepfake classifier with Grad-CAM-based saliency visualisation, (2) a visual captioning module that generates natural language summaries of manipulated regions, and (3) a narrative refinement module that uses a fine-tuned LLM to produce context-aware, user-sensitive explanations. We instantiate and evaluate the framework on the DF40 benchmark, the most diverse deepfake dataset to date. Experiments demonstrate that our system achieves competitive detection performance while providing high-quality explanations aligned with Grad-CAM activations. By unifying prediction and explanation in a coherent, human-aligned pipeline, this work offers a scalable approach to interpretable deepfake detection, advancing the broader vision of trustworthy and transparent AI systems for media forensics.

With increasing regulations on private data usage in AI systems, machine unlearning has emerged as a critical solution for selectively removing sensitive information from trained models while preserving their overall utility. While many existing unlearning methods rely on the *retain data* to mitigate the performance decline caused by forgetting, such data may not always be available (*retain-free*) in real-world scenarios. To address this challenge posed by retain-free unlearning, we introduce **RUAGO**, utilizing adversarial soft labels to mitigate over-unlearning and a generative model pretrained on out-of-distribution (OOD) data to effectively distill the original model’s knowledge. We introduce a progressive sampling strategy to incrementally increase synthetic data complexity, coupled with an inversion-based alignment step that ensures the synthetic data closely matches the original training distribution. Our extensive experiments on multiple benchmark datasets and architectures demonstrate that our approach consistently outperforms existing retain-free methods and achieves comparable or superior performance relative to retain-based approaches, demonstrating its effectiveness and practicality in real-world, data-constrained environments.

Deepfake detection remains a pressing challenge, particularly in real-world settings where smartphone-captured media from digital screens often introduces Moiré artifacts that can distort detection outcomes. This study systematically evaluates state-of-the-art (SOTA) deepfake detectors on Moiré-affected videos—an issue that has received little attention. We collected a dataset of 12,832 videos, spanning 35.64 hours, from Celeb-DF, DFD, DFDC, UADFV, and FF++ datasets, capturing footage under diverse real-world conditions, including varying screens, smartphones, lighting setups, and camera angles. To further examine the influence of Moiré patterns on deepfake detection, we conducted additional experiments using our DeepMoiréFake, referred to as (DMF) dataset, and two synthetic Moiré generation techniques. Across 15 top-performing detectors, our results show that Moiré artifacts degrade performance by as much as 25.4\%, while synthetically generated Moiré patterns lead to a 21.4\% drop in accuracy. Surprisingly, demoiréing methods, intended as a mitigation approach, instead worsened the problem, reducing accuracy by up to 16\%. These findings underscore the urgent need for detection models that can robustly handle Moiré distortions alongside other real-world challenges, such as compression, sharpening, and blurring. By introducing the DMF dataset, we aim to drive future research toward closing the gap between controlled experiments and practical deepfake detection.

Deep neural network (DNN) models are susceptible to adversarial samples in white-box and opaque environments. Although previous studies have shown high attack success rates, coupling DNN models with interpretation models could offer a sense of security when a human expert is involved. However, in white-box environments, interpretable deep learning systems (IDLSes) have been shown to be vulnerable to malicious manipulations. As access to the components of IDLSes is limited in opaque settings, it becomes more challenging for the adversary to fool the system. In this work, we propose aQuery-efficientScore-based opaque attack against IDLSes, which requires no knowledge of the target model and its coupled interpretation model. By continuously refining the adversarial samples created based on feedback scores from the IDLS, our approach effectively reduces the number of model queries and navigates the search space to identify perturbations that can fool the system. We evaluate the attack's effectiveness on four convolutional neural network (CNN) models and two interpretation models, using both ImageNet and CIFAR datasets. Our results show that the proposed approach is query-efficient with a high attack success rate that can reach more than 95%, and an average transferability success rate of 69%. We have also demonstrated that our attack is resilient against various preprocessing defense techniques.

Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns over storing and processing biometric data on servers. This paper introduces Blind-Touch, a novel machine learning-based fingerprint authentication system leveraging homomorphic encryption to address these privacy concerns. Homomorphic encryption allows computations on encrypted data without decrypting. Thus, Blind-Touch can keep fingerprint data encrypted on the server while performing machine learning operations. Blind-Touch combines three strategies to efficiently utilize homomorphic encryption in machine learning: (1) It optimizes the feature vector for a distributed architecture, processing the first fully connected layer (FC-16) in plaintext on the client side and the subsequent layer (FC-1) post-encryption on the server, thereby minimizing encrypted computations; (2) It employs a homomorphic encryption-compatible data compression technique capable of handling 8,192 authentication results concurrently; and (3) It utilizes a clustered server architecture to simultaneously process authentication results, thereby enhancing scalability with increasing user numbers. Blind-Touch achieves high accuracy on two benchmark fingerprint datasets, with a 93.6% F1- score for the PolyU dataset and a 98.2% F1-score for the SOKOTO dataset. Moreover, Blind-Touch can match a fingerprint among 5,000 in about 0.65 seconds. With its privacy-focused design, high accuracy, and efficiency, Blind-Touch is a promising alternative to conventional fingerprint authentication for web and cloud applications.

Text-to-Image models such as Stable Diffusion have shown impressive image generation synthesis, thanks to the utilization of large-scale datasets. However, these datasets may contain sexually explicit, copyrighted, or undesirable content, which allows the model to directly generate them. Given that retraining these large models on individual concept deletion requests is infeasible, fine-tuning algorithms have been developed to tackle concept erasing in diffusion models. While these algorithms yield good concept erasure, they all present one of the following issues: 1) the corrupted feature space yields synthesis of disintegrated objects, 2) the initially synthesized content undergoes a divergence in both spatial structure and semantics in the generated images, and 3) sub-optimal training updates heighten the model's susceptibility to utility harm. These issues severely degrade the original utility of generative models. In this work, we present a new approach that solves all of these challenges. We take inspiration from the concept of classifier guidance and propose a surgical update on the classifier guidance term while constraining the drift of the unconditional score term. Furthermore, our algorithm empowers the user to select an alternative to the erasing concept, allowing for more controllability. Our experimental results show that our algorithm not only erases the target concept effectively but also preserves the model’s generation capability.

Recently, serious concerns have been raised about the privacy issues related to training datasets in machine learning algorithms when including personal data. Various regulations in different countries, including the GDPR grant individuals to have personal data erased, known as ‘the right to be forgotten’ or ‘the right to erasure’. However, there has been less research on effectively and practically deleting the requested personal data from the training set while not jeopardizing the overall machine learning performance. In this work, we propose a fast and novel machine unlearning paradigm at the layer level called layer attack unlearning, which is highly accurate and fast compared to existing machine unlearning algorithms. We introduce the Partial-PGD algorithm to locate the samples to forget efficiently. In addition, we only use the last layer of the model inspired by the Forward-Forward algorithm for unlearning process. Lastly, we use Knowledge Distillation (KD) to reliably learn the decision boundaries from the teacher using soft label information to improve accuracy performance. We conducted extensive experiments with SOTA machine unlearning models and demonstrated the effectiveness of our approach for accuracy and end-to-end unlearning performance.

In the wake of a fabricated explosion image at the Pentagon, an ability to discern real images from fake counterparts has never been more critical. Our study introduces a novel multi-modal approach to detect AI-generated images amidst the proliferation of new generation methods such as Diffusion models. Our method, UGAD, encompasses three key detection steps: First, we transform the RGB images into YCbCr channels and apply an Integral Radial Operation to emphasize salient radial features. Secondly, the Spatial Fourier Extraction operation is used for a spatial shift, utilizing a pre-trained deep learning network for optimal feature extraction. Finally, the deep neural network classification stage processes the data through dense layers using softmax for classification. Our approach significantly enhances the accuracy of differentiating between real and AI-generated images, as evidenced by a 12.64% increase in accuracy and 28.43% increase in AUC compared to existing state-of-the-art methods.

Photo restoration technology enables preserving visual memories in photographs. However, physical prints are vulnerable to various forms of deterioration, ranging from physical damage to loss of image quality, etc. While restoration by human experts can improve the quality of outcomes, it often comes at a high price in terms of cost and time for restoration. In this work, we present the AI-based photo restoration framework composed of multiple stages, where each stage is tailored to enhance and restore specific types of photo damage, accelerating and automating the photo restoration process. By integrating these techniques into a unified architecture, our framework aims to offer a one-stop solution for restoring old and deteriorated photographs. Furthermore, we present a novel old photo restoration dataset because we lack a publicly available dataset for our evaluation.

We present Blind-Match, a novel biometric identification system that leverages homomorphic encryption (HE) for efficient and privacy-preserving 1:N matching. Blind-Match introduces a HE-optimized cosine similarity computation method, where the key idea is to divide the feature vector into smaller parts for processing rather than computing the entire vector at once. By optimizing the number of these parts, Blind-Match minimizes execution time while ensuring data privacy through HE. Blind-Match achieves superior performance compared to state-of-the-art methods across various biometric datasets. On the LFW face dataset, Blind-Match attains a 99.63% Rank-1 accuracy with a 128-dimensional feature vector, demonstrating its robustness in face recognition tasks. For fingerprint identification, Blind-Match achieves a remarkable 99.55% Rank-1 accuracy on the PolyU dataset, even with a compact 16-dimensional feature vector, significantly outperforming the state-of-the-art method, Blind-Touch, which achieves only 59.17%. Furthermore, Blind-Match showcases practical efficiency in large-scale biometric identification scenarios, such as Naver Cloud's FaceSign, by processing 6,144 biometric samples in 0.74 seconds using a 128-dimensional feature vector.

Recent advancements in domain generalization (DG) for face anti-spoofing (FAS) have garnered considerable attention. Traditional methods have focused on designing learning objectives and additional modules to isolate domain-specific features while retaining domain-invariant characteristics in their representations. However, such approaches often lack guarantees of consistent maintenance of domain-invariant features or the complete removal of domain-specific features. Furthermore, most prior works of DG for FAS do not ensure convergence to a local flat minimum, which has been shown to be advantageous for DG. In this paper, we introduce GAC-FAS, a novel learning objective that encourages the model to converge towards an optimal flat minimum without necessitating additional learning modules. Unlike conventional sharpness-aware minimizers, GAC-FAS identifies ascending points for each domain and regulates the generalization gradient updates at these points to align coherently with empirical risk minimization (ERM) gradient updates. This unique approach specifically guides the model to be robust against domain shifts. We demonstrate the efficacy of GAC-FAS through rigorous testing on challenging cross-domain FAS datasets, where it es-tablishes state-of-the-art performance.

Online adaptation to distribution shifts in satellite image segmentation stands as a crucial yet underexplored problem. In this paper, we address source-free and online domain adaptation, i.e., test-time adaptation (TTA), for satellite images, with the focus on mitigating distribution shifts caused by various forms of image degradation. Towards achieving this goal, we propose a novel TTA approach involving two effective strategies. First, we progressively estimate the global Batch Normalization (BN) statistics of the target distribution with incoming data stream. Leveraging these statistics during inference has the ability to effectively reduce domain gap. Furthermore, we enhance prediction quality by refining the predicted masks using global class centers. Both strategies employ dynamic momentum for fast and stable convergence. Notably, our method is backpropagation-free and hence fast and lightweight, making it highly suitable for on-the-fly adaptation to new domain. Through comprehensive experiments across various domain adaptation scenarios, we demonstrate the robust performance of our method.

Deepfake detection is critical in mitigating the societal threats posed by manipulated videos. While various algorithms have been developed for this purpose, challenges arise when detectors operate externally, such as on smartphones, when users take a photo of deepfake images and upload on the Internet. One significant challenge in such scenarios is the presence of Moiré patterns, which degrade image quality and confound conventional classification algorithms, including deep neural networks (DNNs). The impact of Moiré patterns remains largely unexplored for deepfake detectors. In this study, we investigate how camera-captured deepfake videos from digital screens affect detector performance. We conducted experiments using two prominent datasets, CelebDF and FF++, comparing the performance of four state-of-theart detectors on camera-captured deepfake videos with introduced Moiré patterns. Our findings reveal a significant decline in detector accuracy, with none achieving above 68% on average. This underscores the critical need to address Moiré pattern challenges in real-world deepfake detection scenarios.

Online video detection becomes more challenging with higher resolution as computational costs increase proportionally with increasing resolution. To address this issue, we present a novel approach, DynaPP, which arranges object candidate regions into a compact form. DynaPP performs resource-intensive whole-image inference only on sparse key frames, employing reduced resolutions for inference on other frames. Additionally, we propose transforming a 1-stage detector into a dynamic resolution model to facilitate frame inference at reduced resolutions. Here, the dynamic resolution model signifies a model capable of inferring all resolutions, distinguishing itself from typical models by not having restricted inferable resolutions. Unlike prior studies introducing new model structures for multi-resolution models, our work demonstrates that slight modifications to existing models can convert them to dynamic resolution models. DynaPP showcases substantial acceleration in video detection across four representative video datasets: AU-AIR (5.5×), UAVDT (3.67×), VisDrone (2.73×), and ImageNet VID (3.69×), while maintaining a mean average precision with a small loss (≤2.2). Furthermore, we observed that our method achieves a detection acceleration of up to 8.84×, depending on the video clip.

As the focus of space exploration shifts from national agencies to private companies, the interest in space industry has been steadily increasing. With the increasing number of satellites, the risk of collisions between satellites and space debris has escalated, potentially leading to significant property and human losses. Therefore, accurately modeling the orbit is critical for satellite operations. In this work, we propose the Decomposed Attention Segment Recurrent Neural Network (DASR) model, adding two key components, Multi-Head Attention and Tensor Train Decomposition, to SegRNN for orbit prediction. The DASR model applies Multi-Head Attention before segmenting at input data and before the input of the GRU layers. In addition, Tensor Train (TT) Decomposition is applied to the weight matrices of the Multi-Head Attention in both the encoder and decoder. For evaluation, we use three real-world satellite datasets from the Korea Aerospace Research Institute (KARI), which are currently operating: KOMPSAT-3, KOMPSAT-3A, and KOMPSAT-5 satellites. Our proposed model demonstrates superior performance compared to other SOTA baseline models. We demonstrate that our approach has 94.13% higher predictive performance than the second-best model in the KOMPSAT-3 dataset, 89.79% higher in the KOMPSAT-3A dataset, and 76.71% higher in the KOMPSAT-5 dataset.

Recently, the vision transformer (ViT) has achieved remarkable performance in computer vision tasks and has been actively utilized in colorization. Vision transformer uses multi-head self attention to effectively propagate user hints to distant relevant areas in the image. However, despite the success of vision transformers in colorizing the image, heavy underlying ViT architecture and the large computational cost hinder active real-time user interaction for colorization applications. Several research removed redundant image patches to reduce the computational cost of ViT in image classification tasks. However, the existing efficient ViT methods cause severe performance degradation in colorization task since it completely removes the redundant patches. Thus, we propose a novel efficient ViT architecture for real-time interactive colorization, AdaColViT determines which redundant image patches and layers to reduce in the ViT. Unlike existing methods, our novel pruning method alleviates performance drop and flexibly allocates computational resources of input samples, effectively achieving actual acceleration. In addition, we demonstrate through extensive experiments on ImageNet-ctest10k, Oxford 102flowers, and CUB-200 datasets that our method outperforms the baseline methods.

With the growing ubiquity and broad usage, emojis are widely used as a universal visual language, which complements the intentions and emotions beyond the textual data. Despite the critical role of representing emotion, existing emojis neglect the subtle and complex properties of human emotion in that only countable and finite face emojis exist in a categorical manner. In this article, we propose a novel approach to facial emoji generation, which can control the emotional degree of generated emojis for more complex and detailed usage on online conversations. In other words, we develop a new emotion-aware emoji generative adversarial network, which is capable of generating an emoji that expresses a given emotion distribution. In this way, our approach aims to map fine-grained emotions to expressive emojis. Both quantitative and qualitative evaluation demonstrate that our approach can successfully generate highquality emoji-like images by representing a wide range of emotions. To the best of our knowledge, this is the first approach to use the deep generative model from the standpoint of the emoji’s emotional role, which can further promote more interactive and effective online communication.

Deep learning methods have gained increasing attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge$^{+}$, which deceive both the target deep learning model and the coupled interpretation model. We assess the effectiveness of proposed attacks against four deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models. Our experiments include the implementation of attacks using various attack frameworks. We also explore the attack resilience against three general defense mechanisms and potential countermeasures. Our analysis shows the effectiveness of our attacks in terms of deceiving the deep learning models and their interpreters, and highlights insights to improve and circumvent the attacks.

The emergence of hyper-realistic deepfake videos has raised significant concerns regarding their potential misuse. However, prior research on deepfake detection has primarily focused on image-based approaches, with little emphasis on video. With the advancement of generation techniques enabling intricate and dynamic manipulation of entire faces as well as specific facial components in a video sequence, capturing dynamic changes in both global and local facial features becomes crucial in detecting deepfake videos. This paper proposes a novel sequential attentive face embedding, SAFE, that can capture facial dynamics in a deepfake video. The proposed SAFE can effectively integrate global and local dynamics of facial features revealed in a video sequence using contrastive learning. Through a comprehensive comparison with the state-of-the-art methods on the DFDC (Deepfake Detection Challenge) dataset and the FaceForensic++ benchmark, we show that our model achieves the highest accuracy in detecting deepfake videos on both datasets.

Machine learning has evolved through extensive data usage, including personal and private information. Regulations like GDPR highlight the "Right to be forgotten" for user and data privacy. Research in machine unlearning aims to remove specific data from pre-trained models. We introduce a novel two-step unlearning method, UNDO. First, we selectively disrupt the decision boundary of forgetting data at the coarse-grained level. However, this can also inadvertently affect the decision boundary of other remaining data, lowering the overall performance of classification task. Hence, we subsequently repair and refining the decision boundary for each class at the fine-grained level by introducing a loss for maintain the overall performance, while completely removing the class. Our approach is validated through experiments on two datasets, outperforming other methods in effectiveness and efficiency.

Though digital financial systems have provided users with convenient and accessible services, such as supporting banking or payment services anywhere, it is necessary to have robust security to protect against identity misuse. Thus, online digital identity (ID) verification plays a crucial role in securing financial services on mobile platforms. One of the most widely employed techniques for digital ID verification is that mobile applications request users to take and upload a picture of their own ID cards. However, this approach has vulnerabilities where someone takes pictures of the ID cards belonging to another person displayed on a screen, or printed on paper to be verified as the ID card owner. To mitigate the risks associated with fraudulent ID card verification, we present a novel dataset for classifying cases where the ID card images that users upload to the verification system are genuine or digitally represented. Our dataset is replicas designed to resemble real ID cards, making it available while avoiding privacy issues. Through extensive experiments, we demonstrate that our dataset is effective for detecting digitally represented ID card images, not only in our replica dataset but also in the dataset consisting of real ID cards. Our dataset is available at https://github.com/DASH-Lab/idcard_fraud_detection.

In recent times, there has been a notable surge in the amount of vision and sensing/time-series data obtained from drones and satellites.This data can be utilized in various fields, such as precision agriculture, disaster management, environmental monitoring, and others.However, the analysis of such data poses significant challenges due to its complexity, heterogeneity, and scale.Furthermore, it is critical to identify anomalies and maintain/monitor the health of drones and satellite systems to enable the aforementioned applications and sciences.This workshop presents an excellent opportunity to explore solutions that specifically target the detection of anomalies and novel occurrences in drones and satellite systems and their data.For more information, visit our website at https://sites.google.com/view/ansd23.

Deepfake has recently raised a plethora of societal concerns over its possible security threats and dissemination of fake information. Much research on deepfake detection has been undertaken. However, detecting low quality as well as simultaneously detecting different qualities of deepfakes still remains a grave challenge. Most SOTA approaches are limited by using a single specific model for detecting certain deepfake video quality type. When constructing multiple models with prior information about video quality, this kind of strategy incurs significant computational cost, as well as model and training data overhead. Further, it cannot be scalable and practical to deploy in real-world settings. In this work, we propose a universal intra-model collaborative learning framework to enable the effective and simultaneous detection of different quality of deepfakes. That is, our approach is the quality-agnostic deepfake detection method, dubbed QAD. In particular, by observing the upper bound of general error expectation, we maximize the dependency between intermediate representations of images from different quality levels via Hilbert-Schmidt Independence Criterion. In addition, an Adversarial Weight Perturbation module is carefully devised to enable the model to be more robust against image corruption while boosting the overall model’s performance. Extensive experiments over seven popular deepfake datasets demonstrate the superiority of our QAD model over prior SOTA benchmarks.

Existing high-resolution satellite image forgery localization methods rely on patch-based or downsampling-based training. Both of these training methods have major drawbacks, such as inaccurate boundaries between pristine and forged regions, the generation of unwanted artifacts, etc. To tackle the aforementioned challenges, inspired by the high-resolution image segmentation literature, we propose a novel model called HRFNet to enable satellite image forgery localization effectively. Specifically, equipped with shallow and deep branches, our model can successfully integrate RGB and resampling features in both global and local manners to localize forgery more accurately. We perform various experiments to demonstrate that our method achieves the best performance, while the memory requirement and processing speed are not compromised compared to existing methods.

Knowledge distillation (KD) is an effective method for transferring the knowledge of a teacher model to a student model, that aims to improve the latter's performance efficiently. Although generic knowledge distillation methods such as softmax representation distillation and intermediate feature matching have demonstrated improvements with various tasks, only marginal improvements are shown in student networks due to their limited model capacity. In this work, to address the student model's limitation, we propose a novel flexible KD framework, Integrating Matched Features using Attentive Logit in Knowledge Distillation (IMF). Our approach introduces an intermediate feature distiller (IFD) to improve the overall performance of the student model by directly distilling the teacher's knowledge into branches of student models. The generated output of IFD, which is trained by the teacher model, is effectively combined by attentive logit. We use only a few blocks of the student and the trained IFD during inference, requiring an equal or less number of parameters. Through extensive experiments, we demonstrate that IMF consistently outperforms other state-of-the-art methods with a large margin over the various datasets in different tasks without extra computation.

Conventional forgery localizing methods usually rely on different forgery footprints such as JPEG artifacts, edge inconsistency, camera noise, etc., with cross-entropy loss to locate manipulated regions. However, these methods have the disadvantage of over-fitting and focusing on only a few specific forgery footprints. On the other hand, real-life manipulated images are generated via a wide variety of forgery operations and thus, leave behind a wide variety of forgery footprints. Therefore, we need a more general approach for image forgery localization that can work well on a variety of forgery conditions. A key assumption in underlying forged region localization is that there remains a difference of feature distribution between untampered and manipulated regions in each forged image sample, irrespective of the forgery type. In this paper, we aim to leverage this difference of feature distribution to aid in image forgery localization. Specifically, we use contrastive loss to learn mapping into a feature space where the features between un-tampered and manipulated regions are well-separated for each image. Also, our method has the advantage of localizing manipulated region without requiring any prior knowledge or assumption about the forgery type. We demonstrate that our work outperforms several existing methods on three benchmark image manipulation datasets. Code is available at https://github.com/niloy193/CFLNet

Decentralized Identity (DID) is emerging as a new digital identity management scheme that promises users complete control of their personal data and identification without central authority involvement. The World Wide Web Consortium (W3C) has drafted the DID standard and provided reference implementations. We conduct a security analysis of the W3C DID standard and the reference universal resolver implementation, focusing on user privacy in the DID resolving process. The universal resolver is the key component in the architecture that processes DID requests and DID document retrievals. Our analysis demonstrates that privacy issues can arise due to the imprudent design of the universal resolver. Furthermore, we found that side-channels in the DID document caching schemes of real-world DID services can entail privacy concerns. Motivated by our security analysis, we present a novel DID resolving design, called Oblivira, to enable obliviously DID resolving. Oblivira is a secure resolving agent with a small footprint that enforces the universal resolver to resolve requests without knowing their content. We also propose a privacy-preserving DID document caching scheme that eliminates side-channels. Our evaluation results show that Oblivira only incurs approximately 2.6% of overhead on average with different resolver settings (3, 6, and 12 threads).

Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Because of the limited information in low-quality images, detecting low-quality deepfake remains an important challenge. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capability in KD to enable a student network to learn discriminative features from low-quality images effectively. In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher’s and student’s tensors under different views to transfer the teacher tensor’s distribution to the student more efficiently. Our extensive experimental results demonstrate that our approach outperforms state-of-the-art baselines in detecting low-quality compressed deepfake images.

The development of techniques for creating completely synthetic photographic images and videos, as well as the increasing prevalence of disinformation associated with such synthetic media, have sparked interest in the computer community. One such issue is the proliferation of deepfakes. However, much of the research is devoted to outwitting the SOTA in order to generate and detect fabricated images and video. Considering them from the perspective of computer security and human ethics is largely ignored. As such, this workshop aims to provide a forum for researchers to exchange ideas and methodologies, as well as to provide academia and industry with insights into the development and identification of fake media from a computer security perspective.

Super-resolution (SR) aims to recover high-resolution (HR) images from low-resolution (LR) images. Recently, various attempts, e.g., unsupervised SR models and domain-specific SR have achieved outstanding performance for various real-world applications. However, they significantly suffer from low generalization performance when trained on another domain dataset. Furthermore, they often exhibit performance degradation when the model continually learns multiple tasks; so-called catastrophic forgetting degrades the SR performance. In this paper, we are the first to propose a novel approach for continual multi-task SR named Replay-based Continual Representation Learning framework that can be applicable to GAN-based SR models, which utilizes feature memory for preserving the learned features from the previous task. Our experimental results demonstrate the effectiveness of RCRL in continual multi-task SR at improving generalization performance and alleviating catastrophic forgetting.

YouTube videos are one of the most effective platforms for disseminating creative material and ideas, and they appeal to a diverse audience. Along with adults and older children, young children are avid consumers of YouTube materials. Children often lack means to evaluate if a given content is appropriate for their age, and parents have very limited options to enforce content restrictions on YouTube. Young children can thus become exposed to inappropriate content, such as violent, scary or disturbing videos on YouTube. Previous studies demonstrated that YouTube videos can be classified into appropriate or inappropriate for young viewers using video metadata, such as video thumbnails, title, comments, etc. Metadata-based approaches achieve high accuracy, but still have significant misclassifications, due to the reliability of input features. In this paper, we propose a fusion model, called Samba, which uses both metadata and video subtitles for content classification. Using subtitles in the model helps better infer the true nature of a video improving classification accuracy. On a large-scale, comprehensive dataset of 70K videos, we show that Samba achieves 95% accuracy, outperforming other state-of-the-art classifiers by at least 7%. We also publicly release our dataset.

Knowledge distillation (KD) is a powerful technique for improving the performance of a small model by leveraging the knowledge of a larger model. Despite its remarkable performance boost, KD has a drawback with the substantial computational cost of pre-training larger models in advance. Recently, a method called self-knowledge distillation has emerged to improve the model's performance without any supervision. In this paper, we present a novel plug-in approach called Sliding Cross Entropy (SCE) method, which can be combined with existing self-knowledge distillation to significantly improve the performance. Specifically, to minimize the difference between the output of the model and the soft target obtained by self-distillation, we split each softmax representation by a certain window size, and reduce the distance between sliced parts. Through this approach, the model evenly considers all the inter-class relationships of a soft target during optimization. The extensive experiments show that our approach is effective in various tasks, including classification, object detection, and semantic segmentation. We also demonstrate SCE consistently outperforms existing baseline methods.

Recently, dynamic pruning methods have been actively researched, as they have shown very effective and remarkable performance in reducing computation complexity of deep neural networks. Nevertheless, most dynamic pruning methods fail to achieve actual acceleration due to the extra overheads caused by indexing and weight-copying to implement the dynamic sparse patterns for every input sample. To address this issue, we propose Dynamic Pattern-based Pruning Network (DPPNet), which preserves the advantages of both static and dynamic networks. First, our method statically prunes the weight kernel into various sparse patterns. Then, the dynamic convolution kernel is generated via aggregating input-dependent attention weights and static kernels. Unlike previous dynamic pruning methods, our novel method dynamically fuses static kernel patterns, enhancing the kernel's representational power without additional overhead. Moreover, our dynamic sparse pattern enables an efficient process using BLAS libraries, accomplishing actual acceleration. We demonstrate the effectiveness of the proposed DPPNet on CIFAR and ImageNet, outperforming the state-of-the-art methods achieving better accuracy with lower computational cost. For example, on ImageNet classification, ResNet34 utilizing DPP module achieves state-of-the-art performance with 65.6% FLOPs reduction and the inference speed increased by 35.9% without loss in accuracy. Code is available at https://github.com/lee-gwang/DPPNet.

Although the collision of space objects not only incurs a high cost but also threatens human life, the risk of collision between satellites has increased, as the number of satellites has rapidly grown due to the significant interests in many space applications. However, it is not trivial to monitor the behavior of the satellite in real-time since the communication between the ground station and spacecraft is dynamic and sparse, and there is an increased latency due to the long distance. Accordingly, it is strongly required to predict the orbit of a satellite to prevent unexpected contingencies such as a collision. Therefore, the real-time monitoring and accurate orbit prediction are required. Furthermore, it is necessary to compress the prediction model, while achieving a high prediction performance in order to be deployable in the real systems. Although several machine learning and deep learning-based prediction approaches have been studied to address such issues, most of them have applied only basic machine learning models for orbit prediction without considering the size, running time, and complexity of the prediction model. In this research, we propose Selective Tensorized multi-layer LSTM (ST-LSTM) for orbit prediction, which not only improves the orbit prediction performance but also compresses the size of the model that can be applied in practical deployable scenarios. To evaluate our model, we use the real orbit dataset collected from the Korea Multi-Purpose Satellites (KOMPSAT-3 and KOMPSAT-3A) of the Korea Aerospace Research Institute (KARI) for 5 years. In addition, we compare our ST-LSTM to other machine learning-based regression models, LSTM, and basic tensorized LSTM models with regard to the prediction performance, model compression rate, and running time.

Time series anomaly detection is extensively studied in statistics, economics, and computer science. Over the years, numerous methods have been proposed for time series anomaly detection using deep learning-based methods. Many of these methods demonstrate state-of-the-art performance on benchmark datasets, giving the false impression that these systems are robust and deployable in many practical and industrial real-world scenarios. In this paper, we demonstrate that the performance of state-of-the-art anomaly detection methods is degraded substantially by adding only small adversarial perturbations to the sensor data. We use different scoring metrics such as prediction errors, anomaly, and classification scores over several public and private datasets ranging from aerospace applications, server machines, to cyber-physical systems in power plants. Under well-known adversarial attacks from Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) methods, we demonstrate that state-of-the-art deep neural networks (DNNs) and graph neural networks (GNNs) methods, which claim to be robust against anomalies and have been possibly integrated in real-life systems, have their performance drop to as low as 0%. To the best of our understanding, we demonstrate, for the first time, the vulnerabilities of anomaly detection systems against adversarial attacks. The overarching goal of this research is to raise awareness towards the adversarial vulnerabilities of time series anomaly detectors.

Today, various multimedia content can be accessed and shared from any location via the Internet. In addition to normal content, an extensive amount of manipulated multimedia can raise various social concerns. Among the various types of manipulated media, deepfakes can be abused in impersonation or spreading fake information. Therefore, numerous studies have been performed to detect deepfakes to alleviate these concerns, and studies such as FaceForensics++ (FF++) and DeepFake Detection Challenge (DFDC) have sparked these studies by providing deepfake datasets. The deepfake datasets were utilized for supervised learning in conjunction with developing sophisticated neural networks and showed a high detection performance. Since powerful neural networks can learn even subtle details about an image, they must be trained on realistic deepfakes created by advanced deepfake generation technologies to improve the robustness of existing detectors. In order to boost the performance of deepfake detection models, we propose an approach to creating more realistic deepfake images by removing "detectable" artifacts from existing deepfake datasets’ images, including GAN-based images. By applying the proposed method to the original deepfake dataset, we demonstrate that our technique can significantly reduce the detection performance of existing deepfake detectors. Our experimental results show the vulnerability of deployed detectors and pave the way for further improvement.

Anomaly detection in high-dimensional time series is typically tackled using either reconstruction- or forecasting-based algorithms due to their abilities to learn compressed data representations and model temporal dependencies, respectively. However, most existing methods disregard the relationships between features, information that would be extremely useful when incorporated into a model. In this work, we introduce Fused Sparse Autoencoder and Graph Net (FuSAGNet), which jointly optimizes reconstruction and forecasting while explicitly modeling the relationships within multivariate time series. Our approach combines Sparse Autoencoder and Graph Neural Network, the latter of which predicts future time series behavior from sparse latent representations learned by the former as well as graph structures learned through recurrent feature embedding. Experimenting on three real-world cyber-physical system datasets, we empirically demonstrate that the proposed method enhances the overall anomaly detection performance, outperforming baseline approaches. Moreover, we show that mining sparse latent patterns from high-dimensional time series improves the robustness of the graph-based forecasting model. Lastly, we conduct visual analyses to investigate the interpretability of both recurrent feature embeddings and sparse latent representations.

Generating a deep learning-based fake video has become no longer rocket science. The advancement of automated Deepfake (DF) generation tools that mimic certain targets has rendered society vulnerable to fake news or misinformation propagation. In real-world scenarios, DF videos are compressed to low-quality (LQ) videos, taking up less storage space and facilitating dissemination through the web and social media. Such LQ DF videos are much more challenging to detect than high-quality (HQ) DF videos. To address this challenge, we rethink the design of standard deep learning-based DF detectors, specifically exploiting feature extraction to enhance the features of LQ images. We propose a novel LQ DF detection architecture, multi-scale Branch Zooming Network (BZNet), which adopts an unsupervised super-resolution (SR) technique and utilizes multi-scale images for training. We train our BZNet only using highly compressed LQ images and experiment under a realistic setting, where HQ training data are not readily accessible. Extensive experiments on the FaceForensics++ LQ and GAN-generated datasets demonstrate that our BZNet architecture improves the detection accuracy of existing CNN-based classifiers by 4.21% on average. Furthermore, we evaluate our method against a real-world Deepfake-in-the-Wild dataset collected from the internet, which contains 200 videos featuring 50 celebrities worldwide, outperforming the state-of-the-art methods by 4.13%.

Recent advancements in web-based multimedia technologies, such as face recognition web services powered by deep learning, have been significant. As a result, companies such as Microsoft, Amazon, and Naver provide highly accurate commercial face recognition web services for a variety of multimedia applications. Naturally, such technologies face persistent threats, as virtually anyone with access to deepfakes can quickly launch impersonation attacks. These attacks pose a serious threat to authentication services, which rely heavily on the performance of their underlying face recognition technologies. Despite its gravity, deepfake abuse involving commercial web services and their robustness have not been thoroughly measured and investigated. By conducting a case study on celebrity face recognition, we examine the robustness of black-box commercial face recognition web APIs and open-source tools against Deepfake Impersonation (DI) attacks. While the majority of APIs do not make specific claims of deepfake robustness, we find that authentication mechanisms may get built one top of them, nonetheless. We demonstrate the vulnerability of face recognition technologies to DI attacks, achieving respective success rates of 78.0% for targeted (TA) attacks; we also propose mitigation strategies, lowering respective attack success rates to as low as 1.26% for TA attacks with adversarial training.

Recently, an early exit network, which dynamically adjusts the model complexity during inference time, has achieved remarkable performance and neural network efficiency to be used for various applications. So far, many researchers have been focusing on reducing the redundancy of input sample or model architecture. However, they were unsuccessful at resolving the performance drop of early classifiers that make predictions with insufficient high-level feature information. Consequently, the performance degradation of early classifiers had a devastating effect on the entire network performance sharing the backbone. Thus, in this paper, we propose an Efficient Multi-Scale Feature Generation Adaptive Network (EMGNet), which not only reduced the redundancy of the architecture but also generates multi-scale features to improve the performance of the early exit network. Our approach renders multi-scale feature generation highly efficient through sharing weights in the center of the convolution kernel. Also, our gating network effectively learns to automatically determine the proper multi-scale feature ratio required for each convolution layer in different locations of the network. We demonstrate that our proposed model outperforms the state-of-the-art adaptive networks on CIFAR10, CIFAR100, and ImageNet datasets. The implementation code is available at https://github.com/lee-gwang/EMGNet

Generally, object detection on Synthetic-Aperture Radar (SAR) images is known to be more challenging than that in Electro-Optical (EO) satellite images because SAR images have non-negligible speckle noise and require extensive data pre-processing. Nevertheless, object detection in SAR images is important, as SAR imagery can be obtained under severe weather and time conditions. While many recent object detection approaches on SAR imagery focus on improving detection accuracy, few studies focus on improving processing efficiency. In fact, there are significant challenges and trade-offs to achieve both high accuracy and efficiency at the same time. In this work, we introduce SAROD, a novel efficient end-to-end object detection framework on SAR images based on Reinforcement Learning (RL) to balance the tradeoffs. Our proposed model consists of two detectors, coarse and fine-grained detectors, with an RL agent, where RL has not yet been utilized for object detection on SAR imagery. Our model was evaluated on a challenging SAR imagery dataset, achieving performance comparable to state-of-the-art detectors while maintaining high efficiency of source data usage.

Current state-of-the-art autonomous driving technology significantly advanced, leveraging reinforcement learning (RL) algorithms, because it is not easy to apply a rule-based driving method that reflects all the various traffic conditions. Indeed, reinforcement learning can produce the possible optimal driving strategy of urban, rural, and motorway roads in various environmental conditions such as speed limits and school zones. However, it is challenging to adjust the parameters of the reward mechanism in RL, because the driving style of each user is very different. And it takes a massive amount of time and resources to conduct RL by reflecting all complex traffic conditions. However, if RL imitates the driving behavior of an expert, RL algorithm can proceed more quickly. Therefore, we propose a novel imitation learning framework, which combines an expert’s driving behavior with a continuous behavior of an agent. Further, a deep reinforcement learning approach is used to mimic the expert’s driving behavior. Therefore, we propose imitation learning with variational inference and distributional reinforcement learning (IVDR) algorithm. Our results show that IVDR achieves 80% better learning speed than the learning speed of other approaches and outperforms 12% higher in average reward. Our work shows great promise of using RL for autonomous driving and real vehicle driving simulation.

Over the last few decades, artificial intelligence research has made tremendous strides, but it still heavily relies on fixed datasets in stationary environments. Continual learning is a growing field of research that examines how AI systems can learn sequentially from a continuous stream of linked data in the same way that biological systems do. Simultaneously, fake media such as deepfakes and synthetic face images have emerged as significant to current multimedia technologies. Recently, numerous method has been proposed which can detect deepfakes with high accuracy. However, they suffer significantly due to their reliance on fixed datasets in limited evaluation settings. Therefore, in this work, we apply continuous learning to neural networks' learning dynamics, emphasizing its potential to increase data efficiency significantly. We propose Continual Representation using Distillation (CoReD) method that employs the concept of Continual Learning (CL), Representation Learning (RL), and Knowledge Distillation (KD). We design CoReD to perform sequential domain adaptation tasks on new deepfake and GAN-generated synthetic face datasets, while effectively minimizing the catastrophic forgetting in a teacher-student model setting. Our extensive experimental results demonstrate that our method is efficient at domain adaptation to detect low-quality deepfakes videos and GAN-generated images from several datasets, outperforming the-state-of-art baseline methods.

Deep learning-based video manipulation methods have become widely accessible to the masses. With little to no effort, people can quickly learn how to generate deepfake (DF) videos. While deep learning-based detection methods have been proposed to identify specific types of DFs, their performance suffers for other types of deepfake methods, including real-world deepfakes, on which they are not sufficiently trained. In other words, most of the proposed deep learning-based detection methods lack transferability and generalizability. Beyond detecting a single type of DF from benchmark deepfake datasets, we focus on developing a generalized approach to detect multiple types of DFs, including deepfakes from unknown generation methods such as DeepFake-in-the-Wild (DFW) videos. To better cope with unknown and unseen deepfakes, we introduce a Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model training strategy and explores spatial as well as the temporal information in a deepfakes. Through extensive experiments, we show that existing defense methods are not ready for real-world deployment. Whereas our defense method (CLRNet) achieves far better generalization when detecting various benchmark deepfake methods (97.57% on average). Furthermore, we evaluate our approach with a high-quality DeepFake-in-the-Wild dataset, collected from the Internet containing numerous videos and having more than 150,000 frames. Our CLRNet model demonstrated that it generalizes well against high-quality DFW videos by achieving 93.86% detection accuracy, outperforming existing state-of-the-art defense methods by a considerable margin.

Designing a fallback authentication mechanism that is both memorable and strong is a challenging problem because of the trade-off between usability and security. Security questions are popularly used as a fallback authentication method for password recovery.However, they are prone to guessing attacks by users' acquaintances and may be hard to recall. To overcome these limitations, we present PassTag, a hybrid password scheme that takes advantage of both graphical and textual password authentication methods. PassTag combines a user-provided image and a short personalized text description of the image, imagetag, as an authentication secret.Furthermore, PassTag incorporates decoy images to make it difficult to guess the user-provided pictures. We conducted three user studies with 161 participants for up to three months to evaluate the performance of PassTag against security questions. The evaluation results demonstrate that PassTag is significantly stronger against close adversaries and highly memorable (92.6%-95.0%) after one,two, and three months, respectively. Our longitudinal study results show PassTag is a promising alternative for fallback authentication.

Reducing false positives while detecting anomalies is of growing importance for various industrial applications and mission-critical infrastructures, including satellite systems. Undesired false positives can be costly for such systems, bringing the operation to a halt for human experts to determine if the anomalies are true anomalies that need to be mitigated. Although rule-based or machine learning-based anomaly detection approaches have been studied, a tensor-based decomposition method has not been extensively explored. In this work, we introduce an Integrative Tensor-based Anomaly Detection (ITAD) framework to detect anomalies in a satellite system with the goal of minimizing false positives. We construct 3rd-order tensors with telemetry data collected from the Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and calculate the anomaly score using one of the component matrices obtained by applying CANDECOMP/PARAFAC decomposition to detect anomalies. Our result shows that our tensor-based approach outperforms existing methods, achieving higher accuracy and lower false positive rates. And we successfully deployed our anomaly detection system in real KOMPSAT-2 mission operation.

The recent paramount success of the gig economy has introduced new business opportunities in different areas such as food delivery service. However, there are food delivery ride abusers who break the company rule by driving unauthorized vehicles that are not stated in the contract. These abusers are particularly problematic because they break the transportation regulations and unfairly take more orders. However, detecting these abusers are challenging because of lack of labeled datasets and these anomalous abusers do not frequently occur compared to normal riders. Furthermore, sequential patterns of abusing behaviors are not easy to model.

Recent advancements in Generative Adversarial Networks (GANs) enable the generation of highly realistic images, raising concerns about their misuse for malicious purposes. Detecting these GAN-generated images (GAN-images) becomes increasingly challenging due to the significant reduction of underlying artifacts and specific patterns. The absence of such traces can hinder detection algorithms from identifying GAN-images and transferring knowledge to identify other types of GAN-images as well. In this work, we present the Transferable GAN-images Detection framework T-GD, a robust transferable framework for an effective detection of GAN-images. T-GD is composed of a teacher and a student model that can iteratively teach and evaluate each other to improve the detection performance. First, we train the teacher model on the source dataset and use it as a starting point for learning the target dataset. To train the student model, we inject noise by mixing up the source and target datasets, while constraining the weight variation to preserve the starting point. Our approach is a self-training method, but distinguishes itself from prior approaches by focusing on improving the transferability of GAN-image detection. T-GD achieves high performance on the source dataset by overcoming catastrophic forgetting and effectively detecting state-of-the-art GAN-images with only a small volume of data without any metadata information.

Although pronounceability can improve password memorability, most existing password generation approaches have not properly integrated the pronounceability of passwords in their designs. In this work, we demonstrate several shortfalls of current pronounceable password generation approaches, and then propose, ProSemPass, a new method of generating passwords that are pronounceable and semantically meaningful. In our approach, users supply initial input words and our system improves the pronounceability and meaning of the user-provided words by automatically creating a portmanteau. To measure the strength of our approach, we use attacker models, where attackers have complete knowledge of our password generation algorithms. We measure strength in guess numbers and compare those with other existing password generation approaches. Using a large-scale IRB-approved user study with 1,563 Amazon MTurkers over 9 different conditions, our approach achieves a 30% higher recall than those from current pronounceable password approaches, and is stronger than the offline guessing attack limit.

A package tracking number (PTN) is widely used to monitor and track a shipment. Through the lenses of security and privacy, however, a package tracking number can possibly reveal certain personal information, leading to security and privacy breaches. In this work, we examine the privacy issues associated with online package tracking systems used in the top three most popular package delivery service providers (FedEx, DHL, and UPS) in the world and found that those websites inadvertently leak users’ personal data with a PTN. Moreover, we discovered that PTNs are highly structured and predictable. Therefore, customers’ personal data can be massively collected via PTN enumeration attacks. We analyzed more than one million package tracking records obtained from Fedex, DHL, and UPS, and showed that within 5 attempts, an attacker can efficiently guess more than 90% of PTNs for FedEx and DHL, and close to 50% of PTNs for UPS. In addition, we present two practical attack scenarios: 1) to infer business transactions information and 2) to uniquely identify recipients. Also, we found that more than 109 recipients can be uniquely identified with less than 10 comparisons by linking the PTN information with the online people search service, Whitepages.

Detecting fake or disguised face images become much more challenging due to the significant advancements made in machine learning, computer vision, and image processing techniques. In addition, due to the rise of various DeepFakes, fake images can be maliciously used to attack individuals and deter true information. Therefore, it is crucial to building a classifier that accurately distinguishes an individual from different or similar persons. In this preliminary work, we aim to detect a target person's face from different similar individuals, Doppelgangers, leveraging the dataset from Disguised Faces in the Wild (DFW) 2018. We use well-known off-the-shelf face detection classifiers, such as ShallowNet, VGG-16, and Xception to evaluate the classification performance. In order to further improve the detection performance, we apply data augmentation. Our preliminary result shows that the Xception model can classify one from different individuals with a 62% accuracy.

Many companies offer automatic speech recognition or Speech-to-Text APIs for use in diverse applications. However, audio classification algorithms trained with deep neural networks (DNNs) can sometimes misclassify adversarial examples, posing a significant threat to critical applications. In this paper, we present a novel way to create adversarial audio examples using a genetic algorithm. Our algorithm creates adversarial examples by iteratively adding perturbations to the original audio signal. Unlike most white-box adversarial example generations, our approach does not require knowledge about the target DNN's model and parameters (black-box) and heavy computational power of GPU resources. To show the feasibility of the proposed idea, we implement a tool called, Foolgle, using a genetic algorithm that performs untargeted attacks to create adversarial audio examples and evaluate those with the state-of-the-art Google Cloud Speech-to-Text API. Our preliminary experiment results show that Foolgle deceives the API with a success probability of 86%.

To understand users' risk perceptions about sharing their PHR on SNS, we first conducted a qualitative user study by interviewing 16 participants. Next, we conducted a large-scale online user study with 497 participants in the U.S. to validate our qualitative results from the first study. Our study results show that a majority of users do not show strong motivation of sharing PHR on SNS due to several concerns such as misuse/abuse and security issues of shared PHR. In particular, participants are highly concerned about sharing diseases and diagnostic test results than other types of PHRs (e.g., details of hospital visits and medical interviews). However, we found about 55.13% of the participants have the experiences of sharing their health-related information on SNS. Also, we learned that users' sharing behavior for disease data can be significantly influenced by the severity/type of the disease as well as preferred recipients. Based on our findings, we propose a recommendation method to automatically determine whether users' posts can be shared with everyone on SNS by analyzing the keywords frequently occurred in health-related posts. Our implementation using Random Forest achieved an F-measure of 87.4%, indicating that PHR can be restrictively shared with high accuracy, when sharing health-related posts on SNS.

Manufacturing steel requires extremely challenging industrial processes. In particular, predicting the exact time instance of opening and closing tap-holes in a blast furnace has a great influence on steel production efficiency and operating cost, in addition to human safety. However, currently predicting the time to open and close tap-holes of the blast furnace still highly relies on manual human expertise and labor. Also, most of the prior research is limited to indirectly model the level of liquids in the hearth, using complex mathematical models or classical machine learning approaches.

Detecting an anomaly is not only important for many terrestrial applications on Earth but also for space applications. Especially, satellite missions are highly risky because unexpected hardware and software failures can occur due to sudden or unforeseen space environment changes. Anomaly detection and spacecraft health monitoring systems have heavily relied on human expertise to investigate whether they are a true anomaly or not. Also, it is practically infeasible to produce labels on data due to the enormous amount of telemetries generated from a satellite. In this work, we propose a data-driven anomaly detection algorithm for Korea Multi-Purpose Satellite 2 (KOMPSAT-2). We develop a Multivariate Convolution LSTM with Mixtures of Probabilistic Principal Component Analyzers, where our approach uses both neural networks and probabilistic clustering to improve the anomaly detection performance. We evaluated our approach with a total of 22 million telemetry samples collected for 10 months from KOMPSAT-2. We also compare our approach with other state-of-the-art approaches. We show that our proposed approach is 35.8% better in precision, and 18.2% better in F-1 score than the best baseline approach. We plan to deploy our algorithm in the second half of 2019 to actually apply real operation of KOMPSAT-2.

In recent years, interest in measuring air quality has spiked due to rising environmental and health concerns in South Korea. In particular, microfine dust (microdust) is known to cause serious health issues to people. Therefore, measuring and predicting mircodust is an important problem. A typical way of measuring microdust is to use sensors from fixed location. However, this cannot capture the local dynamics of microdust and is limited to accurate measurement near fixed locations. Therefore, there is an immediate need to provide more accurate local air quality measurements in the areas where fixed local sensors are not installed. In this preliminary research, we focus on modeling the air quality pattern in a given local area by using vehicles equipped with cheap IoT sensors, where vehicles move around the area. As a pilot study, We measured the microdust level running experiments for 2 weeks with 3 different cars. Also, we developed an machine learning algorithm to better predict the local air quality using moving sensors. Further, we built an application where measured air quality is reported to the end users. We demonstrated the feasibility of using inexpensive IoT sensors in moving vehicles to provide better local air quality to end users.

In the past, there have been several studies in analyzing password strength and structures. However, there are still many unknown questions to understand what really makes passwords both memorable and strong. In this work, we aim to answer some of these questions by analyzing password dataset through the lenses of data science and machine learning perspectives. We use memorable 3,260 password dataset collected from prior IRB-approved user studies over 3 years and classify passwords into three strength groups using online and offline attack limits. Then, we apply a tensor decomposition to analyze password dataset by constructing a 3rd-order tensor with passwords' syntactic and semantic features. In particular, we used PARAFAC2 tensor decomposition to uncover the main characteristics and features that affect password strength. We quantitatively identified the underlying factors that are more frequently observed in strong and memorable passwords. We hope that our finding can validate widely accepted advice for creating strong passwords and provide useful insights to design a better password suggestion system.

Passwords are widely used for user authentication, but they are often difficult for a user to recall, easily cracked by automated programs, and heavily reused. Security questions are also used for secondary authentication. They are more memorable than passwords, because the question serves as a hint to the user, but they are very easily guessed. We propose a new authentication mechanism, called “life-experience passwords (LEPs).” Sitting somewhere between passwords and security questions, an LEP consists of several facts about a user-chosen life event—such as a trip, a graduation, a wedding, and so on. At LEP creation, the system extracts these facts from the user’s input and transforms them into questions and answers. At authentication, the system prompts the user with questions and matches the answers with the stored ones. We show that question choice and design make LEPs much more secure than security questions and passwords, while the question-answer format promotes low password reuse and high recall. Specifically, we find that: (1) LEPs are 10 9 --10 14 × stronger than an ideal, randomized, eight-character password; (2) LEPs are up to 3 × more memorable than passwords and on par with security questions; and (3) LEPs are reused half as often as passwords. While both LEPs and security questions use personal experiences for authentication, LEPs use several questions that are closely tailored to each user. This increases LEP security against guessing attacks. In our evaluation, only 0.7% of LEPs were guessed by casual friends, and 9.5% by family members or close friends—roughly half of the security question guessing rate. On the downside, LEPs take around 5 × longer to input than passwords. So, these qualities make LEPs suitable for multi-factor authentication at high-value servers, such as financial or sensitive work servers, where stronger authentication strength is needed.

A CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) provides the first line of defense to protect websites against bots and automatic crawling. Recently, audio-based CAPTCHA systems are started to use for visually impaired people in many internet services. However, with the recent improvement of speech recognition and machine learning system, audio CAPTCHAs have come to struggle to distinguish machines from users, and this situation will likely continue to worsen. Unlike conventional CAPTCHA systems, we propose a new conceptual audio CAPTCHA system, combining certain sound, which is only understandable by a machine. Our experiment results demonstrate that the tested speech recognition systems always provide correct responses for our CAPTCHA samples while humans cannot possibly understand them. Based on this computational gap between the human and machine, we can detect bots with their correct responses, rather than their incorrect ones.

Machine learning algorithms including Deep Neural Networks (DNNs) have shown great success in many different areas. However, they are frequently susceptible to adversarial examples, which are maliciously crafted inputs to fool machine learning classifiers. On the other hand, humans cannot distinguish between non-adversarial and adversarial inputs. In this work, we focus on creating adversarial examples to change the polarity of positive and negative reviews with Amazon product review dataset. We introduce a simple heuristics algorithm to construct adversarial product reviews by replacing words with semantically and synthetically similar synonyms. We evaluate our approach against the state-of-the-art CNN-BLSTM classifier. Our preliminary results show the performance drop of the classifier against the adversarial examples. We also present the defense mechanism using adversarial training.

It is no secret that users have difficulty choosing and remembering strong passwords, especially when asked to choose different passwords across different accounts. While research has shed light on password weaknesses and reuse, less is known about user motivations for following bad password practices. Understanding these motivations can help us design better interventions that work with the habits of users and not against them.

We explore a novel application of Question Generation (QG) for authentication use, where questions are widely used to verify user identity for online accounts.In our approach, we prompt users to provide a few sentences about their personal life events.We transform user-provided input sentences into a set of simple fact-based authentication questions.We compared our approach with previous QG systems, and evaluation results show that our approach yielded better performance and the promise of future personalized authentication question generation.

To synchronize clocks between spacecraft in proximity, the Proximity-1 Space Link Interleaved Time Synchronization (PITS) Protocol has been proposed. PITS is based on the NTP Interleaved On-Wire Protocol and is capable of being adapted and integrated into CCSDS Proximity-1 Space Link with minimal modifications. In this work, we discuss the correctness and liveness of PITS. Further, we analyze and evaluate the time synchronization latency performance with various channel error rates in different PITS operational modes.

In this work, we consider information dissemination and sharing in a highly dynamic peer-to-peer (P2P) communication network. In particular, we explore a network coding technique for transmission and a rank based peer selection (RBPS) method for network formation. The combined approach has been shown to improve information sharing and delivery to all users when considering the challenges imposed by the dynamic wireless network environments.