arXiv Machine Learning
StyleAT: Defending Face Recognition Against Semantic Attacks
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arXiv AI
Sep 16
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
arXiv:1906.07927v4 Announce Type: cross Abstract: Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have sh...
arXiv Machine Learning
Jun 11
Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attacks
arXiv:2606. 11615v1 Announce Type: cross Abstract: The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent.
arXiv AI
Aug 3
DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Models
arXiv:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.
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 AI
Jun 24
PHANTOM: A Large-Scale Dataset of Multimodal Adversarial Attacks for Vision-Language Models
arXiv:2606. 24388v1 Announce Type: new Abstract: We introduce a large-scale, open-source dataset of pre-generated adversarial attacks for vision-language models (VLMs).
arXiv Computer Vision
Sep 7
FSPGD: Rethinking Black-box Attacks on Semantic Segmentation
FSPGD introduces a feature-space black-box attack for semantic segmentation that targets intermediate representations rather than just output logits. The method uses a dual loss: an external loss to disrupt cross-model feature alignment and an internal loss to reduce consistency among same-class instances. Experiments on Pascal VOC 2012 and Cityscapes show that FSPGD outperforms existing logit-level and segmentation-specific attacks across CNN and Transformer backbones, and its adversarial examples improve robustness when used for training.