prabhant/synthesizing-robust-adversarial-examples
My entry for ICLR 2018 Reproducibility Challenge for paper Synthesizing robust adversarial examples https://openreview.net/pdf?id=BJDH5M-AW
This project helps machine learning engineers and researchers understand how to create "adversarial examples" that can fool image recognition systems. It takes an image and a target classification, then outputs a modified image that looks normal to humans but is consistently misclassified by AI models, even under various transformations like rotation or cropping. This is valuable for anyone working on the security and reliability of computer vision systems.
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Use this if you need to test the robustness of your image recognition models against sophisticated attack methods or explore the vulnerabilities of deep learning systems to crafted inputs.
Not ideal if you are looking for a general-purpose tool to improve the accuracy or performance of your standard image classification tasks.
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Apr 05, 2018
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