Physical-domain adversarial attacks have been extensively studied in face recognition and object detection, yet the field of palmprint recognition remains largely unexplored. Existing methods generate grayscale patches constrained by the single-channel input of most palmprint models. When deployed on skin, these patches contrast sharply with the surrounding tissue and are readily noticeable to human observers, undermining the covertness required in practical attacks. To address this limitation, we propose a Color Adversarial Patch (CAP) generation algorithm that leverages style transfer principles to produce visually natural color patches while maintaining high attack success rates. The method initiates the patch with a style prior using a pre-trained Contrastive Arbitrary Style Transfer (CAST) model and jointly optimizes adversarial loss, style loss, and smoothness loss within a unified framework. A three-channel averaging strategy is adopted to ensure compatibility with single-channel recognition models during gradient backpropagation. Experiments on the Tongji palmprint dataset show that the generated color patches achieve average cosine similarity values above the decision threshold in physical-domain tests, with peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values significantly higher than those for their grayscale counterparts. Ablation studies validate the indispensable role of each loss component. CAP offers a practical balance between attack effectiveness and visual camouflage, demonstrating the feasibility of concealed physical-domain attacks on palmprint recognition systems.
Yue Liu, Qi Xiong, Lu Leng et al.· Electronics· 0 citations
The human hand serves as a vital biometric modality, with the palm, fingers, and knuckles commonly employed for identity recognition across a range of applications. Despite the abundance of sensitive identity cues present in hand imagery, comprehensive privacy protection, particularly full-hand de-identification, remains underexplored. To address this gap, we introduce a self-semantic-structural-guided de-identification framework that operates directly on a single hand image, eliminating the need for training or optimization. Our method builds upon a pre-trained diffusion-based inpainting model, enhanced with IP-Adapter and ControlNet to incorporate semantic and structural cues. To mitigate identity leakage, we devise a selective semantic injection strategy and a depth-based structural de-identification strategy for each branch. Additionally, to maintain background integrity, we implement a self-adaptive hand-blending technique that seamlessly integrates the de-identified hand into the original scene. Extensive evaluations across multiple datasets and recognition systems validate the method’s effectiveness in preserving image quality, usability, and de-identification fidelity.
Licheng Yan, Yifan Lyu, Weiliang Huang et al.· IEEE Transactions on Informa...· 0 citations
PMRD separates mechanism-consistent factors from modality-specific information and constructs a consensus response domain across three modalities and combines complementary representations through reliability-aware multiview retrieval and supports PMRD as an effective framework for mechanism-aware multimodal drug representation learning.