arXiv AI
Jun 2
Calibrating Uncertainty for Zero-Shot Adversarial CLIP
arXiv:2512. 12997v2 Announce Type: replace-cross Abstract: CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks.
A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP
Adversarial attacks pose a challenge to the reliability of deep learning models, motivating effective detection methods. Existing techniques often rely on attack-specific assumptions, access to adversarial samples, or knowledge of the underlying classifier (white-box).
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arXiv Machine Learning
Aug 6
One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP
arXiv:2505. 19840v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) have achieved widespread success yet remain prone to adversarial attacks.
Hugging Face Trending Papers
Jul 6
LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
Object detectors have many applications in safety-critical systems, but they are known to be sensitive to worst-case perturbations such as adversarial attacks, which limits their applicability in real-world scenarios. Compared with classification, adversarial robustness for object detection has received less attention, and existing methods are often tied to adversarial training, whose performance may not transfer across attacks, perturbation budgets, or architectures.
arXiv AI
Jul 9
LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
arXiv:2607. 06592v1 Announce Type: cross Abstract: Object detectors have many applications in safety-critical systems, but they are known to be sensitive to worst-case perturbations such as adversarial attacks, which limits their applicability in real-world scenarios.
arXiv AI
Jun 18
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.
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 Computer Vision
6d ago
Region-Level Black-Box Defense Against Stealthy Embedding-Space Backdoors in CLIP
arXiv:2609.31558v1 Announce Type: new Abstract: Contrastive Language--Image Pretraining (CLIP) has emerged as a dominant vision backbone due to its strong transferability and zero-shot capabilities....
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 AI
Sep 1
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification
The paper introduces CLIPure, a method for building an adversarially robust zero‑shot image classifier by purifying inputs in the latent space of CLIP. It formulates purification risk using KL divergence between denoising and attack processes via bidirectional SDEs, and proposes two variants: CLIPure‑Diff, which uses a diffusion prior, and CLIPure‑Cos, which relies on cosine similarity. Experiments on CIFAR‑10, ImageNet, and 13 other datasets show significant robustness gains, raising state‑of‑the‑art performance from 71.7% to 91.1% on CIFAR‑10 and from 59.6% to 72.6% on ImageNet.
arXiv AI
Jun 8
Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs
arXiv:2601. 12359v1 Announce Type: cross Abstract: Prompt injection attacks have become an increasing vulnerability for LLM applications, where adversarial prompts exploit indirect input channels such as emails or user-generated content to circumvent alignment safeguards and induce harmful or unintended outputs.
arXiv AI
Jul 17
Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks
arXiv:2607. 14921v1 Announce Type: cross Abstract: Machine learning models are increasingly adapted in various domains.
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.