arXiv AI
Aug 18
Fast Test-Time Refinement for Robust Learned Image Compression
arXiv:2608. 15113v1 Announce Type: cross Abstract: Learned image compression (LIC) has demonstrated remarkable rate-distortion (RD) performance in benign settings.
Revealing Hidden Vulnerabilities in Autoencoders through Gradient Signal Restoration
arXiv:2505. 03646v5 Announce Type: replace-cross Abstract: Adversarial robustness of deep autoencoders (AEs) has received less attention than that of discriminative models, although their compressed latent representations induce ill-conditioned mappings that can amplify small input perturbations and destabilize reconstructions.
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arXiv Machine Learning
Aug 4
DeepDefense: Robust Learning via Layer-Wise Gradient-Feature Alignment
arXiv:2511. 13749v2 Announce Type: replace Abstract: Deep neural networks are known to be vulnerable to adversarial perturbations, which are small, carefully crafted inputs that lead to incorrect predictions.
arXiv Machine Learning
5d ago
Frame the adversary: a structure-aware attack methodology
The paper introduces a new framework for creating frequency‑based adversarial attacks that are grounded in an explicit optimization problem. By defining a perturbation constraint set linked to structured, non‑orthogonal transforms, the authors show that attacks can be generated as weighted σ‒projections onto this set, providing a clear geometric characterization. Experiments on standard datasets demonstrate that these attacks are highly effective across both pretrained and robust models, even on unseen architectures.
Hugging Face Trending Papers
Jun 2
Investigating Adversarial Robustness of Multi-modal Large Language Models
Multi-modal Large Language Models (MLLMs) achieve strong performance on vision-language tasks, but incorporating visual inputs through a vision encoder (e. g.
arXiv Computer Vision
Sep 18
Fast Preemptive Robustification: High-Frequency Response Anti-Aligns Shared Vulnerability
The paper introduces Fast Preemptive Robustification (FPR), a lightweight defense that enhances the robustness of deep neural networks against transferable adversarial attacks. By sharpening Laplacian responses through a single 3×3 channel‑wise convolution, FPR eliminates the need for surrogate models, iterative optimization, or specialized training. Experiments show that FPR lowers untargeted attack success rates by 12.7% and reduces targeted attack success from 10.7% to 4.1%.
arXiv AI
Jun 29
Improving Adversarial Robustness via Activation Amplification and Attenuation
arXiv:2606. 27784v1 Announce Type: cross Abstract: The existence of adversarial attacks is often attributed to the presence of non-robust features in neural networks.
arXiv AI
Jun 6
Beyond Waveform Robustness: Robust Feature-Vocoder Adversarial Attacks on Automatic Speech Recognition
arXiv:2606. 05678v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems have become widely used for multilingual speech-to-text transcription.
arXiv AI
Jun 15
Rethinking Backdoor Adversarial Unlearning through the Lens of Catastrophic Forgetting in Continual Learning
arXiv:2606. 14078v1 Announce Type: cross Abstract: Existing studies reveal that current backdoor defenses exhibit limited robustness and often fail against specific types of attacks.
arXiv AI
Aug 20
Breaking the weakest link to evade vision language models
The paper investigates how Vision Language Models (VLMs) can be fooled by small, human‑imperceptible changes to images. It introduces a gradient‑based attack that targets only the vision encoder, reducing computational cost while still effectively disrupting both untargeted and targeted multimodal alignment. Experiments on open‑source VLMs such as Qwen2.5‑VL, Granite‑Vision, FastVLM, and Phi‑3.5‑Vision demonstrate that these perturbations can significantly alter the models’ textual outputs.
arXiv Machine Learning
Aug 17
Localization then Neutralization: Gradient-guided Token Suppression against Visual Prompt Injection Attack
arXiv:2605. 25194v2 Announce Type: replace Abstract: Adversarial images pose a severe security threat to multimodal large language models through prompt injection.
arXiv Machine Learning
Jul 7
Binary Iterative Method for Non-targeted Adversarial Attack
arXiv:2607. 04145v1 Announce Type: new Abstract: Adversarial attacks guide and provide additional training and test data for both adversarial training and adversarial robustness validation, and expose the 'piecewise linearity' of deep learning based models.
arXiv Machine Learning
Jun 2
A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
arXiv:2606. 02267v1 Announce Type: new Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment.