GAMA: Generative Adversarial Multi-Object Scene Attacks
Authors: Abhishek Aich, Calvin-Khang Ta, Akash Gupta, Chengyu Song, Srikanth Krishnamurthy, Salman Asif, Amit Roy-Chowdhury
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | GAMA triggers 16% more misclassification than state-of-the-art generative approaches in black-box settings where both the classifier architecture and data distribution of the attacker are different from the victim. Our code is available here: https://abhishekaich27.github.io/gama.html. Our extensive experiments on various black-box settings (where victims are multi-label/single-label classifiers and object detectors) show GAMA s state-of-the-art transferability of perturbations (Table 2, 3, 5, 4, 6, and 7). |
| Researcher Affiliation | Collaboration | Abhishek Aich , Calvin-Khang Ta , Akash Gupta, Chengyu Song, Srikanth V. Krishnamurthy, M. Salman Asif, Amit K. Roy-Chowdhury University of California, Riverside, CA, USA. AG is currently with Vimaan AI, USA. |
| Pseudocode | Yes | Algorithm 1: GAMA pseudo-code |
| Open Source Code | Yes | Our code is available here: https://abhishekaich27.github.io/gama.html. The code has been released here: https://github.com/abhishekaich27/GAMA-pytorch |
| Open Datasets | Yes | We use the multi-label datasets PASCAL-VOC [78] and MS-COCO [79] to train generators for the baselines and our method. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] . We provide these details in the supplementary material. (Additionally, the paper mentions evaluation on '50K validation set' of ImageNet, which implies a specific data split for evaluation). |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] . We provide these details in the supplementary material. |
| Software Dependencies | Yes | For the CLIP model, we use the Vi T-B/16 framework [36]. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] . We provide these details in the supplementary material. (This typically includes software environment details). |
| Experiment Setup | Yes | Unless otherwise stated, perturbation budget is set to ℓ 10 for all experiments. We chose the following surrogate models f( ) (Pascal-VOC or MSCOCO pre-trained multi-label classifiers): Res Net152 (Res152) [80], Dense Net169 (Den169) [81], and VGG19 [64]. For the CLIP model, we use the Vi T-B/16 framework [36]. See supplementary material for more training details. |