Summary of Learning to Transform Dynamically For Better Adversarial Transferability, by Rongyi Zhu et al.
Learning to Transform Dynamically for Better Adversarial Transferability
by Rongyi Zhu, Zeliang Zhang, Susan Liang, Zhuo Liu, Chenliang Xu
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates the adversarial transferability of neural networks, which refers to the ability of an adversary to deceive multiple models by crafting imperceptible perturbations. Recent research has identified this phenomenon across various models, highlighting the need for improved methods to enhance transferability. The authors introduce Learning to Transform (L2T), a novel approach that selects optimal transformation combinations from a pool of candidates to diversify input data and boost adversarial transferability. By formulating the selection problem as a trajectory optimization task and employing reinforcement learning, L2T outperforms existing methodologies in enhancing transferability on ImageNet and practical tests with Google Vision and GPT-4V. The code is available for further exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how to trick AI models by adding small changes that are hard to spot. Right now, these tricks can be used across many different AI models. To make it harder for attackers to deceive AI models, researchers have been trying to come up with ways to diversify the data used to train them. A new approach called Learning to Transform (L2T) has been developed that uses a type of learning called reinforcement learning to find the best combinations of transformations to apply to the data. This helps to make it harder for attackers to deceive AI models, making them more secure and practical. |
Keywords
» Artificial intelligence » Gpt » Optimization » Reinforcement learning » Transferability
