ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples (1811.12673v3)

Abstract: Deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples. Specifically, adding imperceptible perturbations to clean images can fool the well trained deep neural networks. In this paper, we propose an end-to-end image compression model to defend adversarial examples: \textbf{ComDefend}. The proposed model consists of a compression convolutional neural network (ComCNN) and a reconstruction convolutional neural network (ResCNN). The ComCNN is used to maintain the structure information of the original image and purify adversarial perturbations. And the ResCNN is used to reconstruct the original image with high quality. In other words, ComDefend can transform the adversarial image to its clean version, which is then fed to the trained classifier. Our method is a pre-processing module, and does not modify the classifier's structure during the whole process. Therefore, it can be combined with other model-specific defense models to jointly improve the classifier's robustness. A series of experiments conducted on MNIST, CIFAR10 and ImageNet show that the proposed method outperforms the state-of-the-art defense methods, and is consistently effective to protect classifiers against adversarial attacks.

• The paper presents the development of ComDefend, a compression-based defense that purges adversarial perturbations to bolster classifier robustness.
• The methodology employs dual CNNs—ComCNN for compression and RecCNN for high-quality reconstruction—to maintain image structure.
• Empirical tests on MNIST, CIFAR-10, and ImageNet confirm that ComDefend outperforms traditional defenses against attacks such as FGSM, BIM, and C&W.

An Efficient Image Compression Model for Mitigating Adversarial Attacks: Analysis of ComDefend

The paper "ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples" presents a novel approach for defending deep neural networks (DNNs) against adversarial examples through an image compression strategy. The core contribution is the development of ComDefend, an end-to-end compression model composed of two convolutional neural networks (CNNs). This model demonstrates the potential to bolster the robustness of classifiers without necessitating structural modifications to the existing networks.

Model Architecture

ComDefend comprises two primary components: the Compression Convolutional Neural Network (ComCNN) and the Reconstruction Convolutional Neural Network (RecCNN). ComCNN performs a pivotal role in maintaining the structural integrity of the input image while effectively purging adversarial perturbations by reducing the pixel representation from 24 bits to 12 bits. Subsequently, RecCNN reconstructs images back to a high-quality state devoid of adversarial noise—a process intricately designed to ensure that the classifier receives a cleansed image for classification.

Empirical Validation

The experimentation involves extensive testing on datasets like MNIST, CIFAR-10, and ImageNet, where ComDefend consistently surpasses existing defense methodologies, demonstrating resilience across various attack scenarios. Notably, through a rigorous evaluation, the model achieves improved accuracy when defending against attacks such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), DeepFool, and Carlini-Wagner (C{content}W), thereby showcasing its efficacy in protecting classifiers from adversarial inputs. The numerical comparisons highlight the method's superiority, often maintaining high classification accuracy even under significant attack strength.

Theoretical and Practical Implications

The theoretical underpinnings of ComDefend rest on its ability to leverage image compression—a tactic that intrinsically reduces the space in which adversarial perturbations can exist. By compressing and reconstructing the image data, the model inherently limits the adversary's search space, thereby mitigating the risk and impact of adversarial attacks. Additionally, the method's compatibility with existing classifiers makes it a practical choice for deployment across various domains where adversarial attacks pose significant risks, such as autonomous driving and security surveillance.

Considerations for Future Research

Future research could enhance ComDefend by optimizing its compression algorithms for different types of media and exploring its integration with other model-specific defensive strategies. Investigating its performance across diverse architectures and expanding its applicability to other domains, such as natural language processing, may also yield fruitful results. Further, refining the compression process could explore adaptive strategies that cater to varying adversarial threat levels.

In conclusion, ComDefend offers a compelling solution for addressing adversarial threats through an innovative image compression mechanism that preserves the structural essence of clean images while mitigating perturbations. Its seamless integration capability and marked performance improvements position it as a valuable tool for enhancing DNN security in adversarial environments.