Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |