Homomorphic encryption-based federated learning (HEFL) strengthens privacy by aggregating encrypted model updates, but it also renders existing backdoor defenses that assume plaintext updates inapplicable. We present HEFLGuard, a single-server backdoor detection framework for HEFL in which the server constructs overlapping validation models from encrypted client groups and clients locally compare logits of the global and validation models on benign samples to expose backdoor behavior. HEFLGuard further combines consistency verification across non-IID validation groups with Byzantine fault-tolerant aggregation of client reports, ensuring robustness under heterogeneous data and Byzantine participants. We evaluate HEFLGuard on seven vision/text benchmarks under three backdoor types across IID and non-IID settings. HEFLGuard consistently reduces ASR from near 100% to nearly the nobackdoor level while keeping the drop in clean accuracy within 2.5%. Compared with prior work, HEFLGuard achieves higher robustness and deployability.

Abstract Deep learning has transformed fields such as computer vision, natural language processing, and audio analysis through its powerful pattern recognition and predictive capabilities. However, the robustness of these models remains a major concern, as they are highly vulnerable to adversarial attacks-subtle, intentional perturbations that lead to incorrect predictions. While recent defenses like adversarial training and defensive distillation aim to improve robustness, they have notable drawbacks, including overfitting and degraded performance under strong attacks. Certified defenses, such as robust training and Randomized Smoothing, offer theoretical guarantees within a specific perturbation radius, yet struggle to reflect real-world robustness due to efficiency bottlenecks and the unpredictable nature of actual adversarial attacks. These challenges reveal a critical gap between current defenses and real-world attack scenarios, highlighting the need for more practical and resilient solutions. To address the challenges of defense-attack gaps and the inefficiency in robust training, we introduce the Explanation-Guided Robust Training Enhancer (EGRTE). EGRTE combines a self-explaining mechanism, which guides adversarial training to focus on generalized features for improved robustness and accuracy, with a masking mechanism that transforms noised data for easier model learning. This approach not only mitigates noise effects, including adversarial perturbations, but also eliminates the need for time-intensive gradient calculations, greatly enhancing training efficiency. Comprehensive experiments on several datasets show EGRTE’s superior certified accuracy and robustness against adversarial attacks, with a 6.24-fold efficiency increase over comparable methods, positioning EGRTE as a highly effective solution for robust and efficient deep learning.

Deep neural networks (DNNs) have revolutionized the field of computer vision like object detection with their unparalleled performance. However, existing research has shown that DNNs are vulnerable to adversarial attacks. In the physical world, an adversary could exploit adversarial patches to implement a Hiding Attack (HA) which patches the target object to make it disappear from the detector, and an Appearing Attack (AA) which fools the detector into misclassifying the patch as a specific object. Recently, many defense methods for detectors have been proposed to mitigate the potential threats of adversarial patches. However, such methods still have limitations in generalization, robustness and efficiency. Most defenses are only effective against the HA, leaving the detector vulnerable to the AA.

While enjoying the great achievements brought by deep learning (DL), people are also worried about the decision made by DL models, since the high degree of non-linearity of DL models makes the decision extremely difficult to understand. Consequently, attacks such as adversarial attacks are easy to carry out, but difficult to detect and explain, which has led to a boom in the research on local explanation methods for explaining model decisions. In this paper, we evaluate the faithfulness of explanation methods and find that traditional tests on faithfulness encounter the random dominance problem, i.e., the random selection performs the best, especially for complex data. To further solve this problem, we propose three trend-based faithfulness tests and empirically demonstrate that the new trend tests can better assess faithfulness than traditional tests on image, natural language and security tasks. We implement the assessment system and evaluate ten popular explanation methods. Benefiting from the trend tests, we successfully assess the explanation methods on complex data for the first time, bringing unprecedented discoveries and inspiring future research. Downstream tasks also greatly benefit from the tests. For example, model debugging equipped with faithful explanation methods performs much better for detecting and correcting accuracy and security problems.

Deep learning has gained tremendous success and great popularity in the past few years. However, deep learning systems are suffering several inherent weaknesses, which can threaten the security of learning models. Deep learning’s wide use further magnifies the impact and consequences. To this end, lots of research has been conducted with the purpose of exhaustively identifying intrinsic weaknesses and subsequently proposing feasible mitigation. Yet few are clear about how these weaknesses are incurred and how effective these attack approaches are in assaulting deep learning. In order to unveil the security weaknesses and aid in the development of a robust deep learning system, we undertake an investigation on attacks towards deep learning, and analyze these attacks to conclude some findings in multiple views. In particular, we focus on four types of attacks associated with security threats of deep learning: model extraction attack, model inversion attack, poisoning attack and adversarial attack. For each type of attack, we construct its essential workflow as well as adversary capabilities and attack goals. Pivot metrics are devised for comparing the attack approaches, by which we perform quantitative and qualitative analyses. From the analysis, we have identified significant and indispensable factors in an attack vector, e.g., how to reduce queries to target models, what distance should be used for measuring perturbation. We shed light on 18 findings covering these approaches’ merits and demerits, success probability, deployment complexity and prospects. Moreover, we discuss other potential security weaknesses and possible mitigation which can inspire relevant research in this area.