APE-GAN: Adversarial Perturbation Elimination with GAN (1707.05474v3)

Abstract: Although neural networks could achieve state-of-the-art performance while recongnizing images, they often suffer a tremendous defeat from adversarial examples--inputs generated by utilizing imperceptible but intentional perturbation to clean samples from the datasets. How to defense against adversarial examples is an important problem which is well worth researching. So far, very few methods have provided a significant defense to adversarial examples. In this paper, a novel idea is proposed and an effective framework based Generative Adversarial Nets named APE-GAN is implemented to defense against the adversarial examples. The experimental results on three benchmark datasets including MNIST, CIFAR10 and ImageNet indicate that APE-GAN is effective to resist adversarial examples generated from five attacks.

• The paper introduces a novel framework using GANs to eliminate adversarial perturbations and boost image classification accuracy.
• APE-GAN employs a generator-discriminator architecture to pre-clean inputs, reducing FGSM-induced errors on CIFAR10 from 77.8% to 26.4%.
• The approach enhances robustness without modifying classifier training, offering a portable defense mechanism for various AI systems.

Summary of APE-GAN: Adversarial Perturbation Elimination with GAN

The paper introduces APE-GAN, a novel framework that leverages Generative Adversarial Networks (GANs) to defend against adversarial examples in image classification tasks. Recognizing the significant vulnerabilities of neural networks to adversarial perturbations, the authors propose a system to mitigate these perturbations before classification, improving the robustness of models on datasets such as MNIST, CIFAR10, and ImageNet.

Core Contributions

APE-GAN's primary contribution lies in its approach: instead of modifying the model's architecture or training process to enhance robustness, it focuses on cleaning the adversarial input itself. This strategy presents a novel angle in adversarial defense, one that relies on preprocessing inputs to eliminate perturbations and recover the original clean signal before classification.

Specifically, APE-GAN employs a generator to estimate the mapping from adversarial examples to their original, non-corrupted counterparts. This is achieved by training a generator-discriminator pair where the generator aims to negate the adversarial noise, and the discriminator differentiates between authentic and regenerated clean inputs.

Experimental Evaluation

The paper evaluates APE-GAN across diverse adversarial generation methods, including L-BFGS, FGSM, DeepFool, JSMA, and various iterations of the CW attack. The results show that APE-GAN successfully reduces error rates significantly on adversarial inputs without substantial degradation in performance on benign inputs. For instance, error rates for FGSM-adversarial samples on CIFAR10 decrease from 77.8% to 26.4% when passed through APE-GAN, demonstrating the model's efficacy in perturbation elimination.

Technical Highlights

APE-GAN makes strategic use of GAN's structure, and optimizes a composite loss function comprising content and adversarial losses. The design ensures that the generator provides outputs closely aligned with the manifold of original clean images, a crucial aspect in maintaining the integrity of the corrected input. The discriminator, meanwhile, refines this process by enforcing a learning dynamic where the generator continuously improves its output quality.

Implications and Future Directions

The implications of APE-GAN are profound, particularly in enhancing the security and reliability of machine learning systems deployed in sensitive applications. By integrating APE-GAN as a preprocessing module, systems can achieve robust defenses independent of the subsequent classifier's architecture or training data.

Looking forward, combining APE-GAN with adversarial training methods could further bolster defense mechanisms. Additionally, investigating the transferability and broader applicability of this framework beyond standard datasets could provide new opportunities for developing resilient AI systems suited to real-world challenges.

The authors have taken a significant step towards mitigating adversarial risks in machine learning, setting the stage for further research and development in adversarial robustness and secure AI architectures.