Excessive Invariance Causes Adversarial Vulnerability (1811.00401v4)

Abstract: Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We show deep networks are not only too sensitive to task-irrelevant changes of their input, as is well-known from epsilon-adversarial examples, but are also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks. We show such excessive invariance occurs across various tasks and architecture types. On MNIST and ImageNet one can manipulate the class-specific content of almost any image without changing the hidden activations. We identify an insufficiency of the standard cross-entropy loss as a reason for these failures. Further, we extend this objective based on an information-theoretic analysis so it encourages the model to consider all task-dependent features in its decision. This provides the first approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities.

• The paper identifies excessive invariance, where deep neural networks fail to account for task-relevant input changes, as a significant cause of adversarial vulnerability.
• It introduces a novel approach utilizing invertible networks and the independence cross-entropy (iCE) loss to encourage networks to differentiate task-relevant features.
• Experimental results demonstrate that the iCE loss effectively reduces adversarial vulnerability, offering a generally applicable method for enhancing DNN security.

Excessive Invariance Causes Adversarial Vulnerability

The paper "Excessive Invariance Causes Adversarial Vulnerability" presents a comprehensive analysis of the adversarial vulnerability exhibited by deep neural networks (DNNs) and introduces novel methodologies to address this issue. The authors distinguish two primary sources of this vulnerability: excessive sensitivity to minor, task-irrelevant changes and excessive invariance to task-relevant changes. The latter, excessive invariance, forms the focal point of their investigation.

The paper begins with a discussion of adversarial examples, where DNNs often fail when confronted with slightly perturbed or altered input data out of their training distribution. It targets the notion that while DNNs are notoriously sensitive to small, irrelevant perturbations – an area well-explored in previous literature under the $\epsilon$-bounded adversary approach – they also exhibit excessive invariance. This invariance allows for significant modifications to class-specific input content without corresponding changes in the network’s hidden activations, allowing adversarial actors to exploit vast regions of the input space undetected.

The authors illustrate this invariance with experiments on MNIST and ImageNet datasets, revealing the ability to manipulate class-specific content within an image without altering the network's output activations. This suggests that conventional DNNs are missing substantial amounts of task-relevant information due to inadequate loss functions, primarily cross-entropy loss, which does not sufficiently incentivize the network to account for all task-dependent features.

To address this issue, the paper introduces a novel architecture utilizing invertible networks that preserve information throughout layers, allowing explicit access to the decision space to identify and rectify excessive invariance. In this architecture, the paper proposes a new objective function – the independence cross-entropy loss (iCE) – which leverages an information-theoretic extension to encourage networks to differentiate essential class-specific features from nuisances.

Strong experimental evidence supports the claims made by the authors. The independence cross-entropy loss demonstrably reduces the adversarial vulnerability by forcing a more informative feature separation, thus overcoming the insecurities caused by excessive invariance. Furthermore, this method is shown to be generally applicable across various problem domains and network architectures.

The practical implications of this research are profound, as securing DNNs against adversarial attacks is essential for widespread adoption in sensitive applications such as autonomous driving and healthcare AI systems. Theoretically, the work advances our understanding of feature representation in DNNs and highlights the importance of investigating the trade-offs between invariance and sensitivity.

Looking forward, this paper paves the way for new architectures that integrate information theory with network training techniques to enhance robustness against adversarial attacks. Future developments may include optimization of the iCE strategy, examination of other types of invariance within network architectures, and the broader application of these findings to real-world adversarial threats beyond image classification tasks. The paper’s contribution to understanding and mitigating adversarial vulnerability marks a significant step in developing more secure and reliable AI systems.