Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models (1808.01688v2)

Abstract: The prediction accuracy has been the long-lasting and sole standard for comparing the performance of different image classification models, including the ImageNet competition. However, recent studies have highlighted the lack of robustness in well-trained deep neural networks to adversarial examples. Visually imperceptible perturbations to natural images can easily be crafted and mislead the image classifiers towards misclassification. To demystify the trade-offs between robustness and accuracy, in this paper we thoroughly benchmark 18 ImageNet models using multiple robustness metrics, including the distortion, success rate and transferability of adversarial examples between 306 pairs of models. Our extensive experimental results reveal several new insights: (1) linear scaling law - the empirical $\ell_2$ and $\ell_\infty$ distortion metrics scale linearly with the logarithm of classification error; (2) model architecture is a more critical factor to robustness than model size, and the disclosed accuracy-robustness Pareto frontier can be used as an evaluation criterion for ImageNet model designers; (3) for a similar network architecture, increasing network depth slightly improves robustness in $\ell_\infty$ distortion; (4) there exist models (in VGG family) that exhibit high adversarial transferability, while most adversarial examples crafted from one model can only be transferred within the same family. Experiment code is publicly available at \url{https://github.com/huanzhang12/Adversarial_Survey}.

• The paper reveals a linear trade-off between accuracy and robustness as lower classification errors result in reduced adversarial resilience.
• The analysis shows that model architecture, rather than size, is crucial for robustness, with improvements seen in models like ResNet and DenseNet.
• The study underscores high adversarial transferability in the VGG family, prompting new approaches for robust defense strategies.

Robustness and Accuracy in Deep Image Classification Models: An Analytical Perspective

In the paper titled "Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models," the authors present a thorough investigation into the trade-offs between robustness and accuracy across various deep neural network (DNN) architectures. The paper evaluates 18 models from different network architectures, such as AlexNet, VGG, Inception, ResNet, DenseNet, MobileNet, and NASNet, using robustness metrics like adversarial distortion and transferability of adversarial examples, among others.

Key Insights and Results

1. Linear Scaling Law: The paper demonstrates an intriguing linear relationship between adversarial distortion metrics and the logarithm of classification error. This finding suggests that as one attempts to achieve lower classification errors, the robustness of the models, as measured by adversarial perturbations, tends to decrease linearly. This insight is crucial for understanding the inherent trade-offs when designing models that aim to balance accuracy and robustness.
2. Impact of Model Architecture: Rather than model size, the authors find that the architecture plays a dominant role in determining robustness. While deeper versions of the same architectural family (e.g., ResNet, DenseNet) improve robustness slightly in terms of ℓ∞ distortion, this observation opens up discussions on the structural components of robustness in neural networks.
3. Adversarial Transferability: The transferability of adversarial attacks is analyzed through 306 model pairs. A notable finding is that adversarial examples from the VGG family possess high transferability across other models, while adversarial examples of most other models tend to remain within familial boundaries. This characteristic reveals potential pathways for counter-efforts such as model architecture reverse-engineering in black-box settings.
4. Evaluative Methodologies: The paper meticulously utilizes both adversarial attacks and attack-agnostic evaluation methods. The Fast Gradient Sign Method (FGSM), Iterative FGSM (I-FGSM), C&W attack, and EAD-L1 attack measure the vulnerability of models, while CLEVER score provides intrinsic robustness estimates absent of specific attacks, emphasizing the paper’s comprehensive analysis framework.

Implications for Future Research and AI Development

This paper presents significant implications for both practical applications and theoretical exploration within the AI community. Practically, the results signal alertness to potential vulnerabilities in applying highly accurate models to sensitive real-world contexts, such as autonomous driving, where robustness could outweigh the tune of accuracy. Theoretically, the highlighted linear scaling law prompts further exploration into the mathematical underpinnings of DNNs, potentially guiding new principles in robust model design.

Speculation on Future Developments

Given the results, one could anticipate a surge in research aimed at mitigating the cost of robustness deterioration while attaining high accuracy, driving the development of novel network architectures or learning paradigms. Additionally, the insights into the transferability of adversarial examples may lead to more sophisticated adversarial training techniques centered on improving cross-model robustness.

In conclusion, this paper not only dissects existing paradigms of accuracy and robustness in DNNs but also provides a clarion call to the research community to innovate towards reconciling these often competing attributes. As AI continues to percolate through critical infrastructural sectors, such findings underscore the urgency and importance of advancing robust and reliable machine learning solutions.