Ziping Ye

Qian Ma, Ziping Ye, Shagufta Mehnaz

To investigate the effectiveness of the model explanation in detecting adversarial examples, we reproduce the results of two papers, Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples and Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples. And then conduct experiments and case studies to identify the limitations of both works. We find that Attacks Meet Interpretability(AmI) is highly dependent on the selection of hyperparameters. Therefore, with a different hyperparameter choice, AmI is still able to detect Nicholas Carlini's attack. Finally, we propose recommendations for future work on the evaluation of defense techniques such as AmI.

Qian Ma, Ziping Ye

Adversarial examples are a major problem for machine learning models, leading to a continuous search for effective defenses. One promising direction is to leverage model explanations to better understand and defend against these attacks. We looked at AmI, a method proposed by a NeurIPS 2018 spotlight paper that uses model explanations to detect adversarial examples. Our study shows that while AmI is a promising idea, its performance is too dependent on specific settings (e.g., hyperparameter) and external factors such as the operating system and the deep learning framework used, and such drawbacks limit AmI's practical usage. Our findings highlight the need for more robust defense mechanisms that are effective under various conditions. In addition, we advocate for a comprehensive evaluation framework for defense techniques.