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BadCLIP++: Stealthy and Persistent Backdoors in Multimodal Contrastive Learning [73.46118996284888]
Research on backdoor attacks against multimodal contrastive learning models faces two key challenges: stealthiness and persistence.<n>We propose BadCLIP++, a unified framework that tackles both challenges.<n>For stealthiness, we introduce a semantic-fusion QR micro-trigger that embeds imperceptible patterns near task-relevant regions.<n>For persistence, we stabilize trigger embeddings via radius shrinkage and centroid alignment.
arXiv Detail & Related papers (2026-02-19T08:31:16Z)
Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness [9.013874391203453]
Adversarial examples reveal critical vulnerabilities in deep neural networks by exploiting their sensitivity to imperceptible input perturbations.<n>In this work, we investigate an architectural approach to adversarial robustness by embedding group-equivariant convolutions.<n>These layers encode symmetry priors that align model behavior with structured transformations in the input space, promoting smoother decision boundaries.
arXiv Detail & Related papers (2025-10-17T19:26:58Z)
DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection [3.523860679419481]
This research presents DeepTrust, a novel metaheuristic that arranges flexible classifiers into an ordered sequence.<n>In the Robust Android Malware Detection competition at the 2025 IEEE Conference SaTML, DeepTrust secured the first place and achieved state-of-the-art results.<n>This is accomplished while maintaining the highest detection rate on non-adversarial malware and a false positive rate below 1%.
arXiv Detail & Related papers (2025-10-14T09:10:23Z)
CBPNet: A Continual Backpropagation Prompt Network for Alleviating Plasticity Loss on Edge Devices [16.318540474216416]
We argue that the reduction in plasticity stems from a lack of update vitality in underutilized parameters during the training process.<n>We propose the Continual Backpropagation Prompt Network (CBPNet), an effective and parameter efficient framework designed to restore the model's learning vitality.
arXiv Detail & Related papers (2025-09-19T09:16:54Z)
T2V-OptJail: Discrete Prompt Optimization for Text-to-Video Jailbreak Attacks [67.91652526657599]
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arXiv Detail & Related papers (2025-05-10T16:04:52Z)
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We propose a robust test-time prompt tuning (R-TPT) for vision-language models (VLMs)<n>R-TPT mitigates the impact of adversarial attacks during the inference stage.<n>We introduce a plug-and-play reliability-based weighted ensembling strategy to strengthen the defense.
arXiv Detail & Related papers (2025-04-15T13:49:31Z)
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Quantized Neural Networks (QNNs) have emerged as a promising solution for reducing model size and computational costs.<n>In this work, we demonstrate that adversarial patches remain highly transferable across quantized models.<n>We propose Quantization-Aware Defense Training with Randomization (QADT-R) to enhance resilience against highly transferable patch-based attacks.
arXiv Detail & Related papers (2025-03-10T08:43:36Z)
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We propose a novel defense method called Test-Time Adversarial Prompt Tuning (TAPT) to enhance the inference robustness of CLIP against visual adversarial attacks. TAPT is a test-time defense method that learns defensive bimodal (textual and visual) prompts to robustify the inference process of CLIP. We evaluate the effectiveness of TAPT on 11 benchmark datasets, including ImageNet and 10 other zero-shot datasets.
arXiv Detail & Related papers (2024-11-20T08:58:59Z)
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This research paper presents an innovative approach that leverages Convolutional Neural Networks (CNNs) with a 2D baseline model, enhanced by the cutting-edge Cat Swarm Optimization algorithm. The results unveil an impressive accuracy score of $98.4%$, marking a significant enhancement in APT detection across various attack stages.
arXiv Detail & Related papers (2024-08-30T14:11:12Z)
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers [0.8348593305367524]
Deep learning techniques for automatic modulation classification (AMC) of wireless signals are vulnerable to adversarial attacks. This poses a severe security threat to the DL-based wireless systems, specifically for edge applications of AMC. We address the joint problem of developing optimized DL models that are also robust against adversarial attacks.
arXiv Detail & Related papers (2024-04-11T06:15:01Z)
FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
arXiv Detail & Related papers (2024-03-26T08:51:23Z)
Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization [53.04697800214848]
Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models. AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting. We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
arXiv Detail & Related papers (2023-10-19T13:13:41Z)
Adaptive Feature Alignment for Adversarial Training [56.17654691470554]
CNNs are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. We propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths. Our method is trained to automatically align features of arbitrary attacking strength.
arXiv Detail & Related papers (2021-05-31T17:01:05Z)
GOAT: GPU Outsourcing of Deep Learning Training With Asynchronous Probabilistic Integrity Verification Inside Trusted Execution Environment [0.0]
Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a range of applications ranging from self-driving cars to COVID-19 treatment discovery. To support the computational power necessary to learn a DNN, cloud environments with dedicated hardware support have emerged as critical infrastructure. Various approaches have been developed to address these challenges, building on trusted execution environments (TEE)
arXiv Detail & Related papers (2020-10-17T20:09:05Z)
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness [79.47619798416194]
Learn2Perturb is an end-to-end feature perturbation learning approach for improving the adversarial robustness of deep neural networks. Inspired by the Expectation-Maximization, an alternating back-propagation training algorithm is introduced to train the network and noise parameters consecutively.
arXiv Detail & Related papers (2020-03-02T18:27:35Z)

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