Vulnerability of Neural Network Interpretations to Universal Adversarial Perturbations

Keskeiset käsitteet

Neural network interpretations using gradient-based saliency maps are susceptible to universal adversarial perturbations that can significantly alter the interpretation across a large fraction of input samples.

Tiivistelmä

The paper investigates the vulnerability of gradient-based interpretation methods for neural networks to universal adversarial perturbations (UPIs). The authors propose two approaches to design such UPIs:

1. UPI-Grad: A gradient-based optimization method to find a universal perturbation that maximizes the change in gradient-based feature maps across input samples.

2. UPI-PCA: A principal component analysis (PCA)-based approach that approximates the solution to the UPI-Grad optimization problem by computing the top singular vector of the gradients of the interpretation distance function.

The authors demonstrate the effectiveness of the proposed UPI methods through numerical experiments on standard image datasets like MNIST, CIFAR-10, and Tiny-ImageNet, using various neural network architectures like VGG-16 and MobileNet. The results show that the UPIs can significantly alter the gradient-based interpretations of the neural networks, often performing as well as per-image adversarial perturbations designed specifically for each input. The authors also show that classification-based universal adversarial perturbations can have a notable impact on the interpretation of neural networks as well.

The key insights are:

• Gradient-based interpretation methods for neural networks are vulnerable to universal adversarial perturbations.
• The proposed UPI-Grad and UPI-PCA methods can effectively design such universal perturbations to alter the interpretation across a large fraction of input samples.
• The UPIs can achieve comparable or better performance than per-image adversarial perturbations in changing the neural network interpretations.
• Classification-based universal adversarial perturbations also have a significant impact on the interpretation of neural networks.

Tilastot

The average dissimilarity between original and perturbed gradient-based interpretations can be as high as 0.849 for the Integrated Gradients method on MNIST using the proposed UPI-PCA approach.
The UPI-Grad method achieves an average dissimilarity of 0.676 on the Integrated Gradients interpretation of MobileNet on the Tiny-ImageNet dataset.
The classification-based universal adversarial perturbations can achieve an average dissimilarity of 0.412 on the Integrated Gradients interpretation of MobileNet on the CIFAR-10 dataset.

Lainaukset

"Neural network interpretations using gradient-based saliency maps are susceptible to universal adversarial perturbations that can significantly alter the interpretation across a large fraction of input samples."
"The proposed UPI-Grad and UPI-PCA methods can effectively design such universal perturbations to alter the interpretation across a large fraction of input samples."
"The UPIs can achieve comparable or better performance than per-image adversarial perturbations in changing the neural network interpretations."

Tärkeimmät oivallukset

Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations

by Haniyeh Ehsa... klo arxiv.org 04-23-2024

https://arxiv.org/pdf/2212.03095.pdf

Syvällisempiä Kysymyksiä

How can the proposed UPI methods be extended to other interpretation techniques beyond gradient-based methods

The proposed Universal Perturbation for Interpretation (UPI) methods can be extended to other interpretation techniques beyond gradient-based methods by adapting the optimization framework to accommodate different types of interpretation schemes. Instead of focusing solely on gradient-based saliency maps, the optimization problem can be reformulated to target feature importance maps generated by other interpretation methods, such as occlusion-based techniques, activation maximization, or layer-wise relevance propagation. By modifying the objective function to maximize the perturbation's impact on the interpretation output of these methods, UPIs can be designed to alter a broader range of interpretation techniques.

What are the potential implications of the vulnerability of neural network interpretations to universal adversarial perturbations in real-world applications

The vulnerability of neural network interpretations to universal adversarial perturbations has significant implications in real-world applications, especially in critical domains where the reliability and trustworthiness of model interpretations are paramount. In fields like healthcare, finance, and autonomous systems, where decisions based on neural network predictions have high stakes, the presence of universal adversarial perturbations can lead to misinterpretations and erroneous conclusions. This vulnerability could result in incorrect diagnoses in medical imaging, financial mismanagement in algorithmic trading, or safety hazards in autonomous vehicles. Addressing this vulnerability is crucial to ensure the robustness and integrity of neural network interpretations in practical applications.

Can the insights from this work be leveraged to develop more robust and reliable interpretation methods for neural networks

The insights from this work can be leveraged to develop more robust and reliable interpretation methods for neural networks by incorporating adversarial robustness into the interpretation process. By considering the potential impact of universal adversarial perturbations on interpretation outputs, researchers can design interpretation techniques that are more resilient to such attacks. This may involve integrating adversarial training strategies into the interpretation pipeline, developing countermeasures to detect and mitigate adversarial perturbations, or exploring alternative interpretation methods that are less susceptible to adversarial attacks. By enhancing the robustness of interpretation methods, practitioners can have greater confidence in the reliability and accuracy of neural network interpretations, leading to more trustworthy decision-making based on AI models.