Metric Learning for Adversarial Robustness (1909.00900v2)

Abstract: Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and find that the attack causes the internal representation to shift closer to the "false" class. Motivated by this observation, we propose to regularize the representation space under attack with metric learning to produce more robust classifiers. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. Quantitative experiments show improvement of robustness accuracy by up to 4% and detection efficiency by up to 6% according to Area Under Curve score over prior work. The code of our work is available at https://github.com/columbia/Metric_Learning_Adversarial_Robustness.

Metric Learning for Adversarial Robustness: Insights and Implications

In the research paper titled "Metric Learning for Adversarial Robustness," the authors investigate the susceptibility of deep networks to adversarial attacks and propose a novel defense method leveraging metric learning to enhance robustness and detection capabilities of classifiers. The adversarial vulnerability of deep networks remains a pressing concern, especially given their critical role in applications demanding high safety and reliability standards.

Overview

The authors conduct an empirical analysis of latent representations subjected to adversarial attacks employing Projected Gradient Descent (PGD), a state-of-the-art attack method. Their findings reveal that attacks tend to shift internal representations of adversarial samples away from their true class and closer to false class distributions, thereby compromising the network's decision boundaries. This observation forms the basis for integrating metric learning into robustifying classifier models against adversarial inputs.

Metric learning, particularly Triplet Loss (a well-established strategy in the regime of supervised metric learning), is utilized to refine the representation space of classifiers under attack. This paradigm involves sampling triplet data points consisting of anchor, positive, and negative examples, to lace adversarial samples near their corresponding natural samples while distancing them from samples of incorrect classes. Through Triplet Loss Adversarial (TLA) training, the proposed approach enhances adversarial robustness by up to 4% in terms of classification accuracy and improves detection efficiency of adversarial samples by up to 6%, as evidenced across various datasets including MNIST, CIFAR-10, and Tiny ImageNet.

Strong Numerical Results and Claims

The empirical evaluation exhibits significant improvements in robustness and the effectiveness of adversarial sample detection:

• Robust classification accuracy increased by up to 4% compared to prior robust training methods under white-box attacks.
• Adversarial sample detection efficiency boosted by up to 6% using the Area Under Curve (AUC) metric.
• TLA training demonstrates superior performance across varied model architectures on different datasets, ensuring its generalizability.

Implications and Future Directions

The paper underscores the potential of metric learning frameworks in establishing robust machine learning models, specifically in securing classifier decision boundaries against adversarial threats. The advancements achieved with TLA could precipitate the development of novel training methodologies incorporating sophisticated metric learning techniques, such as N-pair loss, which may further ameliorate robustness without complicating network architectures.

Practically, these findings could influence safety-critical applications employing deep learning by enhancing their reliability under adversarial conditions. The theoretic implications suggest a promising avenue for AI research focused on robust deep learning systems capable of consistently resisting adversarial perturbations. The presented methodology could catalyze further scholarly and industrial pursuits that intertwine metric learning and adversarial defense strategies, prompting multidisciplinary collaborations to bolster AI security and trustworthiness.

In conclusion, "Metric Learning for Adversarial Robustness" presents a compelling case for integrating metric learning into adversarial defense frameworks, with experimental validations attesting to its practicability and efficacy in mitigating adversarial risks—facilitating advancements toward safe, reliable, and robust AI systems.