Fooling Neural Network Interpretations via Adversarial Model Manipulation (1902.02041v3)

Abstract: We ask whether the neural network interpretation methods can be fooled via adversarial model manipulation, which is defined as a model fine-tuning step that aims to radically alter the explanations without hurting the accuracy of the original models, e.g., VGG19, ResNet50, and DenseNet121. By incorporating the interpretation results directly in the penalty term of the objective function for fine-tuning, we show that the state-of-the-art saliency map based interpreters, e.g., LRP, Grad-CAM, and SimpleGrad, can be easily fooled with our model manipulation. We propose two types of fooling, Passive and Active, and demonstrate such foolings generalize well to the entire validation set as well as transfer to other interpretation methods. Our results are validated by both visually showing the fooled explanations and reporting quantitative metrics that measure the deviations from the original explanations. We claim that the stability of neural network interpretation method with respect to our adversarial model manipulation is an important criterion to check for developing robust and reliable neural network interpretation method.

• The paper demonstrates how adversarial model manipulation fools neural interpretation methods like Grad-CAM and LRP while maintaining high prediction accuracy with only a 1-2% loss on Top-5 ImageNet.
• It evaluates both passive strategies, such as location and Top-k% fooling, and active fooling that swaps interpretations between classes to mislead saliency maps.
• The findings highlight the need for robust interpretability frameworks to resist model manipulation and ensure trustworthy explanations in AI systems.

Insights into Adversarial Manipulation of Neural Network Interpretations

The paper "Fooling Neural Network Interpretations via Adversarial Model Manipulation" presents a critical examination of the robustness of prominent neural network interpretation methods against adversarial model manipulation. Neural interpretations, often visualized through saliency maps, provide explanations of model predictions and are crucial for understanding complex neural networks. Yet, their vulnerability to adversarial attacks that alter these interpretations raises concerns about their reliability.

Research Scope and Methodology

The authors focus on assessing the susceptibility of interpretation methods, particularly gradient or saliency map-based approaches such as Layer-wise Relevance Propagation (LRP), Grad-CAM, and SimpleGrad. The primary inquiry is whether these methods can be fooled through adversarial manipulation of the model parameters, without significantly impacting the model's prediction accuracy. The paper introduces adversarial model manipulation—a fine-tuning technique aimed at altering interpretation results rather than classification outcomes.

Two types of fooling strategies were proposed:

1. Passive Fooling: This aims to generate uninformative interpretations and includes three specific strategies:
   • Location Fooling that misleads the saliency maps to emphasize irrelevant image regions.
   • Top-$k$\% Fooling that minimizes the relevance of pixels with the original top-$k$\% importances.
   • Center-mass Fooling that shifts the center of mass of relevance significantly from crucial parts.
2. Active Fooling: This involves swapping interpretation heatmaps between two classes of interest, effectively deceiving where the model attributes its decision grounds.

The implementation involved fine-tuning well-known pre-trained models (VGG19, ResNet50, and DenseNet121) on the ImageNet dataset, incorporating adversarial penalties in the learning objective.

Results Summary

The empirical results demonstrate that these interpretation methods are indeed vulnerable to adversarial model manipulations:

• Across different architectures, passive fooling successfully diverted saliency maps from significant regions to irrelevant parts with negligible accuracy loss (around 1-2% on Top-5 ImageNet accuracy).
• Active fooling distinctly swapped target interpretations between two object classes while maintaining the classifier's overall performance, albeit with some complexity-specific challenges in models like DenseNet121.
• Notably, manipulation affecting one interpretation method transferred partially to others—an effect attributed to the common reliance on gradients.

Theoretical and Practical Implications

This research underscores the importance of robustness in neural network interpretation methods, not only against standard adversarial attacks on inputs but also against subtler manipulations involving model alterations. The implications are twofold:

1. Theoretical: It necessitates a refined understanding of how interpretation methods operate vis-à-vis model parameters and the resilience of these methods to parameter perturbations, akin to input space adversarial strategies.
2. Practical: Given the increasing reliance on interpretations for model validation and bias detection in sensitive applications, ensuring robustness against such adversarial manipulations is imperative for deploying trustworthy AI systems.

Future Directions

The findings advocate for the development of more robust interpretability frameworks that consider adversarial manipulation resistance as a primary criterion. Enhanced training procedures that integrate stability checks akin to adversarial training could form a pivotal part of this endeavor. Furthermore, a systematic exploration of the transferability of fooling effects, particularly in complex model architectures, could offer deeper insights into designing secure interpretative methods.

In essence, this paper not only highlights a vulnerability in current neural interpretation strategies but also sets a foundational challenge for the AI research community: ensuring that model explanations are as dependable as the predictions they seek to clarify.