We unveil discernible temporal (or historical) trajectory imprints resulting from adversarial example (AE) attacks. Standing in contrast to existing studies, which focus on spatial (or static) imprints within the targeted underlying victim models, we present a novel temporal paradigm for understanding these attacks. These imprints are encapsulated within a single loss metric, spanning universally across diverse tasks such as classification and regression, and modalities including image, text, and audio. Recognizing the distinct nature of loss between adversarial and clean examples, we exploit this temporal imprint for AE detection by proposing (Traceable Adversarial Temporal Imprints). TRAIT operates under minimal assumptions without prior knowledge of attacks, thereby framing the detection challenge as a one-class classification problem. However, detecting AEs is still challenged by significant overlaps between the constructed synthetic losses of adversarial and clean examples due to the absence of ground truth for incoming inputs. TRAIT addresses this challenge by converting the synthetic loss into a spectrum signature, using the technique of Fast Fourier Transform to highlight the discrepancies, drawing inspiration from the temporal nature of the imprints, analogous to time-series signals. Across 12 AE attacks including SMACK (USENIX Sec'2023), TRAIT demonstrates consistent outstanding performance across comprehensively evaluated modalities (image, text, audio), tasks (classification and regression), datasets (nine datasets), and model architectures (e.g., ResNeXt50, BERT, RoBERTa, AudioNet). In all scenarios, TRAIT achieves an AE detection accuracy exceeding 97%, often around 99%, while maintaining a false rejection rate of 1%. TRAIT remains effective under the formulated strong adaptive attacks.

Federated Learning (FL) enables collaborative machine learning training while preserving data privacy. However, reliance on a central server of the typical FL confronts the risk of single server failure. Decentralized Federated Learning (DFL) emerges as a promising distributed framework, allowing clients to directly share models without server intervention, thereby addressing this challenge. Nevertheless, due to its decentralized nature, DFL is highly susceptible to Byzantine attacks orchestrated by malicious clients. Existing Byzantine-resilient DFL algorithms, though few, remain vulnerable to adaptive attacks due to their heavy reliance ongradient checksof local models, which can be adaptively manipulated by intelligent adversaries. To tackle this issue, we propose a DFL aggregation scheme called FORCE (Byzantine-Resilient Decentralized Federated Learning via Game-Theoretic Contribution Aggregation). Drawing inspiration from the Shapley value in game theory, FORCE shifts from gradient-checking approaches to employ a universal metric, the loss of the local model—independent of specific gradients, to identify potentially malicious clients. Moreover, to reduce the computational overhead of FORCE as the number of neighboring clients scales up, we propose a computationlightweight variant, FORCE−, which is optimized through approximating Shapley value computation. This variant becomes more scalable for resource-restricted DFL clients that are also aggregators. Experimental results on three diverse datasets (two image modalities and one textual modality datasets) under three existing attacks demonstrate that FORCE outperforms existing state-of-the-art Byzantine-resilient DFL aggregation methods, effectively defending against Byzantine attacks.

Model watermarking is a widely adopted mechanism for protecting deep learning (DL) model intellectual property (IP). Black-box verifiable watermarking typically involves injecting backdoors that cause the model to produce predetermined outputs for specific inputs. In contrast, white-box verifiable watermarking uses steganographic techniques to embed watermarks into weight parameters or activation values. However, the former poses new security risks, while the latter often lacks robustness against removal techniques. In this paper, we propose a latent model watermarking, constructing upon the model Division and Union operating concept, dubbed as DUO, leveraging the strengths of two watermarking methods above while eliminating each shortcoming. Once the model owner or provider embeds a watermark into the model using watermark data, the watermarked model is divided into two parts: the main model, which corresponds to the primary task and is made publicly available, and a small sub-network privately reserved by the owner. The watermark resides latently within the main model and can only be activated through the private sub-network (the reserved parameters) when they are united. Consequently, DUO does not adversely affect the performance of the main model on its primary task and does not induce any security risks, even in the presence of watermark data. We extensively validate DUO on four benchmark datasets (CIFAR-10, ImageNette, CIFAR-100, and Tiny-ImageNet) using various model architectures, including standardized ResNet and VGG. The results affirm its capability to accurately verify model ownership without compromising model accuracy. It exhibits a 100% detection accuracy on pirated/positive testing models (96 models are tested) with a 0% false positive rate on normal/negative testing models (64 models are tested). Due to its latent nature, DUO is both effective and robust, capable of withstanding a wide range of state-of-the-art watermark laundering including severe model fine-tuning and pruning. We further evaluate and demonstrate that DUO remains robust against adaptive attacks, even when both the watermark data and the reserved parameters are known to the adversary.

In contrast to prevalent Federated Learning (FL) privacy inference techniques such as generative adversarial networks attacks, membership inference attacks, property inference attacks, and model inversion attacks, we devise an innovative privacy threat: the Data Distribution Decompose Attack on FL, termedDecaf. This attack enables an honest-but-curious FL server to meticulously profile the proportion of each class owned by the victim FL user, divulging sensitive information like local market item distribution and business competitiveness. The crux ofDecaflies in the profound observation that the magnitude of local model gradient changes closely mirrors the underlying data distribution, including the proportion of each class.Decafaddresses two crucial challenges: accurately identify the missing/null class(es) given by any victim user as a premise and then quantify the precise relationship between gradient changes and each remaining non-null class. Notably,Decafoperates stealthily, rendering it entirely passive and undetectable to victim users regarding the infringement of their data distribution privacy. Experimental validation on five benchmark datasets (MNIST, FASHION-MNIST, CIFAR-10, FER-2013, and SkinCancer) employing diverse model architectures, including customized convolutional networks, standardized VGG16, and ResNet18, demonstratesDecaf’s efficacy. Results indicate its ability to accurately decompose local user data distribution, regardless of whether it is IID or non-IID distributed. Specifically, the dissimilarity measured using$L_{\infty }$distance between the distribution decomposed byDecafand ground truth is consistently below 5% when no null classes exist. Moreover,Decafachieves 100% accuracy in determining any victim user’s null classes, validated through formal proof.

The right to be forgotten has incentivized machine unlearning, but a key challenge persists: the lack of reliable methods to verify unlearning conducted by model providers. This gap facilitates dishonest model providers to deceive data contributors. Current approaches often rely on invasive methods like backdoor injection. However, it poses security concerns and is also inapplicable to legacy data—already released data. To tackle this challenge, this work initializes the first non-invasive unlearning verification framework which operates at triple-granularity (class-, volume-, sample-level) to assess the data facticity and volume integrity of machine unlearning. In this paper, we propose a framework, named TruVRF, encompasses three Unlearning-Metrics, each tailored to counter different types of dishonest model providers or servers (Neglecting Server, Lazy Server, Deceiving Server). TruVRF leverages non-invasive model sensitivity to enable multi-granularity verification of unlearning. Specifically, Unlearning-Metric-I checks if the removed class matches the data contributor’s unlearning request, Unlearning-Metric-II measures the amount of unlearned data, and Unlearning-Metric-III validates the correspondence of a specific unlearned sample with the requested deletion. We conducted extensive evaluations of TruVRF efficacy across three datasets, and notably, we also evaluated the effectiveness and computational overhead of TruVRF in real-world applications for the face recognition dataset. Our experimental results demonstrate that TruVRF achieves robust verification performance: Unlearning-Metric-I and -III achieve over 90% verification accuracy on average against dishonest servers, while Unlearning-Metric-II maintains an inference deviation within 4.8% to 8.2%. Additionally, TruVRF demonstrates generalizability across diverse conditions, including varying numbers of unlearned classes and sample volumes. Significantly, TruVRF is applied to two state-of-theart unlearning frameworks: SISA [3] (presented at Oakland’21) and Amnesiac Unlearning [18], representing exact and approximate unlearning methods, respectively, which affirm TruVRF’s practicality. In addition, we conducted extensive evaluations around TruVRF, including ablation experiments, trade-offs in computational overhead, and the robustness of model sensitivity, among others.

Byzantine-robust federated learning (FL) endeavors to empower service providers in acquiring a precise global model, even in the presence of potentially malicious FL clients. While considerable strides have been taken in the development of robust aggregation algorithms for FL in recent years, their efficacy is confined to addressing particular forms of Byzantine attacks, and they exhibit vulnerabilities when confronted with a spectrum of attack vectors. Notably, a prevailing issue lies in the heavy reliance of these algorithms on the examination of local model gradients. It is worth noting that an attacker possesses the ability to manipulate a carefully chosen small gradient of a model within a context where there could be millions of gradients available, thereby facilitating adaptive attacks. Drawing inspiration from the foundational Shapley value methodology in game theory, we introduce an effective FL scheme namedCareFL. This scheme is designed to provide robustness against a spectrum of state-of-the-art Byzantine attacks. Unlike approaches that rely on the examination of gradients,CareFLemploys a universal metric, the loss of the local model—independent of specific gradients, to identify potentially malicious clients. Specifically, in each aggregation round, the FL server trains a reference model using a small auxiliary dataset— the auxiliary dataset can be removed with a slight defense degradation trade-off. It employs the Shapley value to assess the contribution of each client-submitted model in minimizing the global model loss. Subsequently, the server selects client models closer to the reference model in terms of Shapley values for the global model update. To reduce the computational overhead ofCareFLwhen the number of clients is relatively scaled-up, we construct its variant, namelyCareFL+ generally by grouping clients. Extensive experimentation conducted on well-established MNIST and CIFAR-10 datasets, encompassing diverse model architectures, including AlexNet, demonstrates thatCareFLconsistently achieves accuracy levels comparable to those attained under attack-free conditions when faced with five formidable attacks.CareFLand CareFL+ outperform six existing state-of-the-art Byzantine-robust FL aggregation methods, includingFLTrust, across both IID and non-IID data distribution settings.

An accurate phase unwrapping algorithm is presented for reconstructing the true phase field for deformation interferograms. This algorithm is a combination of reliability sorting and residue mask, while introducing more reliable unwrapping fiducial information. In tests conducted with a pyramidal phase image, a complex simulated interferogram, and a real deformation interferogram, the proposed algorithm successfully unwraps the images with perfect precision. This algorithm is also shown to be superior to the prevailing improved Goldstein's residue-cut algorithm in terms of precision.

A novel noise reduction scheme for synthetic aperture radar (SAR) interferograms based on the wavelet packet transform (WPT) and the Wiener filter is introduced in this letter. First, by employing the WPT in the spatial frequency domain, the real and imaginary parts of the complex noisy interferogram are decomposed, respectively, and the wavelet coefficients are obtained. Then, for these coefficients, Wiener filtering is adopted to remove noise. This scheme can filter noise adaptively according to the local noise level, without requiring any a priori information. By using a simulated noisy interferogram and two ENVISAT advanced synthetic aperture radar C-band interferograms, the performance of this scheme, in terms of noise reduction and fringes preservation, is reported and compared with other filter algorithms. The experimental results demonstrate the effectiveness of the proposed scheme.