Towards Evaluating the Robustness of Neural Networks (1608.04644v2)

Abstract: Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input $x$ and any target classification $t$, it is possible to find a new input $x'$ that is similar to $x$ but classified as $t$. This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from $95\%$ to $0.5\%$. In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with $100\%$ probability. Our attacks are tailored to three distance metrics used previously in the literature, and when compared to previous adversarial example generation algorithms, our attacks are often much more effective (and never worse). Furthermore, we propose using high-confidence adversarial examples in a simple transferability test we show can also be used to break defensive distillation. We hope our attacks will be used as a benchmark in future defense attempts to create neural networks that resist adversarial examples.

• The paper introduces three novel attack algorithms based on L0, L2, and L∞ metrics that achieve a 100% success rate across various datasets.
• It rigorously evaluates defensive distillation, demonstrating only marginal improvements in robustness against adversarial examples.
• High-confidence adversarial examples and transferability tests highlight the need for more resilient defenses in security-critical applications.

Towards Evaluating the Robustness of Neural Networks

In the domain of machine learning, neural networks have emerged as powerful tools capable of delivering state-of-the-art performance across a multitude of tasks, including image recognition, speech processing, and natural language understanding. However, their vulnerability to adversarial examples—inputs intentionally designed to cause misclassification—poses a significant challenge, especially for applications in security-critical areas such as autonomous driving and malware detection.

Summary and Contributions

Carlini and Wagner's paper critically examines the robustness claims of defensive distillation, a technique proposed to enhance the resilience of neural networks against adversarial attacks. The authors introduce three novel attack algorithms tailored to the $L_0$, $L_2$, and $L_{\infty}$ distance metrics, demonstrating that these attacks can successfully generate adversarial examples for both distilled and undistilled neural networks with a 100% success rate. These attacks are not only effective but often superior to previous methods.

The key contributions of this paper include:

1. Development of Novel Attacks: The introduction of three new attack algorithms optimized for $L_0$, $L_2$, and $L_{\infty}$ distance metrics that outperform existing methods.
2. Evaluation of Defensive Distillation: A thorough evaluation revealing that defensive distillation provides marginal robustness improvements.
3. High-Confidence Adversarial Examples: The proposal to use high-confidence adversarial examples in transferability tests, further demonstrating the limitations of defensive distillation.

Numerical Results and Robustness Evaluation

The authors' attacks exhibit strong performance across various datasets, including MNIST, CIFAR-10, and ImageNet. Notably, their $L_2$ attack achieves an average perturbation of 1.36 on MNIST and 0.17 on CIFAR-10, compared to Deepfool's 2.11 and 0.85, respectively. Similarly, their $L_{\infty}$ attack requires minimal perturbations, achieving 0.13 on MNIST and 0.0092 on CIFAR-10, demonstrating substantial improvements over the iterative gradient sign method.

When applied to defensively distilled networks, the results underscore that defensive distillation does not significantly mitigate adversarial vulnerability. For instance, the $L_2$ attack records perturbations of 1.7 on MNIST and 0.36 on CIFAR-10—figures still significantly low, suggesting minimal security gains from distillation.

Implications and Future Directions

Carlini and Wagner's findings have critical implications for the security and deployment of neural networks. Their work underscores the necessity for more robust and adaptive defense mechanisms that can withstand sophisticated adversarial attacks. Notably, the introduction of high-confidence adversarial examples as a metric for evaluating robustness can be a valuable tool for future research.

The theoretical implications suggest a need for deeper insights into the internal mechanics of neural networks and their vulnerability pathways. Practically, this could translate into improved, more resilient architectures that can be confidently deployed in security-critical applications. The transferability of attacks from one model to another also invites exploration into cross-model robustness and the design of universally secure models.

Conclusion

The evaluation conducted by Carlini and Wagner shifts the narrative surrounding defensive distillation, revealing it as an insufficient standalone defense. Their work compels the research community to explore more sophisticated and comprehensive approaches to fortifying neural networks against adversarial threats. As AI continues to permeate critical areas of technology, ensuring the robustness and security of these systems becomes not only a technical challenge but a necessary endeavor for safeguarding the future of artificial intelligence applications.