Summary of Bb-patch: Blackbox Adversarial Patch-attack Using Zeroth-order Optimization, by Satyadwyoom Kumar et al.
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization
by Satyadwyoom Kumar, Saurabh Gupta, Arun Balaji Buduru
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 The proposed paper aims to address the limitations of deep learning models by developing novel adversarial attack strategies for image classification tasks. The authors demonstrate that current state-of-the-art models are vulnerable to attacks that manipulate pixel-level information, which can lead to significant performance degradation and potentially catastrophic failures in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new approach is being developed to improve the robustness of deep learning models against adversarial attacks. Currently, many image classification models fail when faced with manipulated images. The goal is to create more reliable models that work well even when the input data has been tampered with. |
Keywords
» Artificial intelligence » Deep learning » Image classification
