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..
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 | Venue PDF | 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. |