Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
Authors: Shuai Jia, Bangjie Yin, Taiping Yao, Shouhong Ding, Chunhua Shen, Xiaokang Yang, Chao Ma
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the FFHQ and Celeb A-HQ datasets show that the proposed Adv-Attribute method achieves the state-of-the-art attacking success rates while maintaining better visual effects against recent attack methods. |
| Researcher Affiliation | Collaboration | Shuai Jia1 Bangjie Yin2 Taiping Yao2 Shouhong Ding2 Chunhua Shen3 Xiaokang Yang1 Chao Ma1 1 Mo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University 2 Youtu Lab, Tencent 3 Zhejiang University |
| Pseudocode | Yes | Algorithm 1: The proposed Adv-Attribute. Input: Source image xo Xo; Victim image xt Xt; Targeted FR model f( ); Pre-trained Style GAN generator Gs; Adversarial noise generator Gi a, i {1, ..., N}. Output: Adversarial examples xadv; |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of its source code or a link to a code repository. |
| Open Datasets | Yes | In our work, we choose two public facial datasets for evaluation: 1) FFHQ [29] is a high-quality human face dataset, which consists of more than 70,000 high-quality human face images with variations of age and ethnicity. 2) Celeb A-HQ [33] is constructed as a higher-quality version of the Celeb A dataset [34]. |
| Dataset Splits | No | The paper specifies the selection of 100 source faces and 10 target faces to construct 1000 source-target pairs for the impersonation attack. However, it does not explicitly provide the training, validation, and test splits for the dataset used to train their Adv-Attribute framework or the attribute noise generators. |
| Hardware Specification | Yes | The overall framework is implemented in Py Torch on one NVIDIA Tesla P40 GPU. |
| Software Dependencies | No | The paper mentions 'implemented in Py Torch' and 'trained using Adam optimizer [36]' but does not provide specific version numbers for PyTorch or any other software libraries or dependencies. |
| Experiment Setup | Yes | The attribute noise generators are trained using Adam optimizer [36] with an initial learning rate of 0.0001. |