This paper proposes MAFT, a novel multimodal automated fact-checking system capable of handling content in any combination of text, images, videos, and audio. The core idea behind our system is the textualization of multimodal content using various machine learning techniques. MAFT comprehensively analyzes this textualized content along with external information collected via web APIs by large language models (LLMs). MAFT generates interpretable fact-checking reports that include not only verification results but also a detailed verification process. With its adaptability and ability to automatically verify multimodal content, MAFT contributes to the fight against the spread of multimodal misinformation.

In this paper, we present a deepfake detection algorithm specifically designed for electronic Know Your Customer (eKYC) systems. To ensure the reliability of eKYC systems against deepfake attacks, it is essential to develop a robust deepfake detector capable of identifying both face swapping and face reenactment, while also being robust to image degradation. We address these challenges through three key contributions: (1) Our approach evaluates the video’s authenticity by detecting temporal inconsistencies in identity vectors extracted by face recognition models, leading to comprehensive detection of both face swapping and face reenactment. (2) In addition to processing video input, the algorithm utilizes a registered image (assumed to be genuine) to calculate identity discrepancies between the input video and the registered image, significantly improving detection accuracy. (3) We find that employing a face feature extractor trained on a larger dataset enhances both detection performance and robustness against image degradation. Our experimental results show that our proposed method accurately detects both face swapping and face reenactment comprehensively and is robust against various forms of unseen image degradation. Our source code is publicly available https://github.com/TaikiMiyagawa/DeepfakeDetection4eKYC.

We study test-time domain adaptation for audio deepfake detection (ADD), addressing three challenges: (i) source-target domain gaps, (ii) limited target dataset size, and (iii) high computational costs. We propose an ADD method using prompt tuning in a plug-in style. It bridges domain gaps by integrating it seamlessly with state-of-the-art transformer models and/or with other fine-tuning methods, boosting their performance on target data (challenge (i)). In addition, our method can fit small target datasets because it does not require a large number of extra parameters (challenge (ii)). This feature also contributes to computational efficiency, countering the high computational costs typically associated with large-scale pre-trained models in ADD (challenge (iii)). We conclude that prompt tuning for ADD under domain gaps presents a promising avenue for enhancing accuracy with minimal target data and negligible extra computational burden.

This paper develops a certified defense for deep neural network (DNN) based content based image retrieval (CBIR) against adversarial examples (AXs). Previous works put their effort into certified defense for classification to improve certified robustness, which guarantees that no AX to cause misclassification exists around the sample. Such certified defense, however, could not be applied to CBIR directly because the goals of adversarial attack against classification and CBIR are completely different. To develop the certified defense for CBIR, we first define new certified robustness of CBIR, which guarantees that no AX that changes the ranking of CBIR exists around the query or candidate images. Then, we propose computationally tractable verification algorithms that verify whether the certified robustness of CBIR is achieved by utilizing upper and lower bounds of distances between feature representations of perturbed and non-perturbed images. Finally, we propose new objective functions for training feature extraction DNNs that increases the number of inputs that satisfy the certified robustness of CBIR by tightening the upper and lower bounds. Experimental results show that our objective functions significantly improve the certified robustness of CBIR than existing methods.

We assess the vulnerabilities of deep face recognition systems for images that falsify/spoof multiple identities simultaneously. We demonstrate that, by manipulating the deep feature representation extracted from a face image via imperceptibly small perturbations added at the pixel level using our proposed method, one can fool a face verification system into recognizing that the face image belongs to multiple different identities with a high success rate. One characteristic of the UAXs crafted with our method is that they are universal (identity-agnostic); they are successful even against identities not known in advance. For a certain deep neural network, we show that we are able to spoof almost all tested identities (99%), including those not known beforehand (not included in training). Our results indicate that a multiple-identity attack is a real threat and should be taken into account when deploying face recognition systems.

Face identification systems based on deep neural networks (DNNs) have been widely introduced in various areas. The key component of the systems is a feature extractor trained to map a face image into a feature space to distinguish a large number of unspecified individuals. Several works revealed that the feature extractor is vulnerable to carefully crafted adversarial examples. These works indicate that we could manipulate the outputs of the feature extractor by perturbing a source image. However, they might require large perturbations to ensure that the perturbed image is recognized incorrectly. This raises a question: how can we craft adversarial examples which leads miss-identification into a target identity with high-confidence and small perturbation? In this paper, to obtain adversarial examples with high confidence and small perturbation, we first introduce a condition which adversarial examples against the face identification systems should satisfy, then introduce a new method called GlassMasq to create adversarial examples based on the condition. In our evaluations, we demonstrate that our generated adversarial examples can deceive the face identification systems with higher confidence and at most 62.8% smaller perturbation than existing methods. The proposed method enables us to evaluate robustness of the face identification system against adversarial examples in a more appropriate manner.

This paper develops differentially private mechanisms for $\chi^2$ test of independence. While existing works put their effort into properly controlling the type-I error, in addition to that, we investigate the type-II error of differentially private mechanisms. Based on the analysis, we present unit circle mechanism: a novel differentially private mechanism based on the geometrical property of the test statistics. Compared to existing output perturbation mechanisms, our mechanism improves the dominated term of the type-II error from $O(1)$ to $O(\exp(-\sqrt{N}))$ where $N$ is the sample size. Furthermore, we introduce novel procedures for multiple $\chi^2$ tests by incorporating the unit circle mechanism into the sparse vector technique and the exponential mechanism. These procedures can control the family-wise error rate (FWER) properly, which has never been attained by existing mechanisms.