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Published May 30, 2020

Robustness to Unforeseen Adversarial Attacks

Kyle Polich and Daniel Kang delve into the intricacies of making machine learning models robust against unforeseen adversarial attacks, discussing the challenges of deployment, data management, and the development of defenses against both traditional and novel threats.
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  • Robustness Defined

    and explore the concept of robustness in machine learning, particularly in the face of adversarial attacks. Robustness refers to a model's ability to maintain performance despite small perturbations in input data, which can be adversarial in nature 1. explains that even minor changes, like altering a few pixels in an image, can mislead models, highlighting the challenge of ensuring robustness 2.

    If we perturb a few pixels in the image by a small amount, then the machine learning model should still classify that image as a cat.

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    The discussion emphasizes the need for models to withstand both known and unforeseen adversarial attacks 1.

       

    Challenges

    Achieving robustness in machine learning models is fraught with challenges, particularly due to the complexity of input spaces and evolving adversarial threats. notes that existing defenses often fail against novel attack vectors, such as weather-based attacks, resulting in drastically reduced accuracy 3. This highlights the ongoing "arms race" between developing attacks and defenses.

    Existing defenses basically get 0% accuracy on them.

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    also discusses the broader challenges of deploying machine learning models in real-world scenarios, where cost and distribution shifts can impact performance 4.

       

    Strategies

    Exploring strategies to enhance robustness, discusses joint adversarial training, which aims to defend against multiple attacks simultaneously. However, initial results indicate that this approach does not significantly outperform training against a single attack 5. This suggests a potential trade-off in focusing defenses on specific attack types, which may lead to vulnerabilities against others.

    Standard methods of joint adversarial training do not do much better than training against a single attack.

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    emphasizes the importance of benchmarks to drive progress in developing more comprehensive defense strategies 6.

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