Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (1805.06605v2)

Abstract: In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks. Defense-GAN is trained to model the distribution of unperturbed images. At inference time, it finds a close output to a given image which does not contain the adversarial changes. This output is then fed to the classifier. Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure. It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples. We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies. Our code has been made publicly available at https://github.com/kabkabm/defensegan

• The paper presents Defense-GAN, a framework that leverages GANs to pre-process inputs and effectively mitigate adversarial perturbations.
• The method trains a GAN on clean data to regenerate inputs that conform to the true data distribution, countering attacks like FGSM, BIM, and DeepFool.
• Experimental results show up to 95.7% classification success on MNIST and significant improvements on CIFAR-10, underscoring its potential in secure AI applications.

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

The paper entitled "Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models" by Pouya Samangouei, Maya Kabkab, and Rama Chellappa, explores an innovative strategy to safeguard machine learning models from adversarial attacks. Adversarial attacks present significant challenges in the field of machine learning, where an attacker introduces subtle perturbations to input data that can mislead classifiers, leading to incorrect outputs. The proposed Defense-GAN framework leverages the robust generative capabilities of Generative Adversarial Networks (GANs) to enhance the security and reliability of machine learning classifiers.

Summary of the Methodology

Defense-GAN involves the integration of GANs in the pre-processing stage of the classifier's input data. Specifically, a GAN model is trained to generate data that ideally resembles the distribution of the actual, unperturbed input samples. When faced with adversarial inputs, the Defense-GAN attempts to reconstruct these inputs in a way that their adversarial nature is mitigated. The reconstructed inputs are then passed to the classifier, which improves robustness against the attacks.

The adversarial defense mechanism works as follows:

1. Training a GAN: The GAN is trained on the non-adversarial training data to learn the distribution of the original dataset. This enables the GAN to generate samples that closely follow the manifold of legitimate data.
2. Reconstruction of Inputs: During inference, given an input instance (potentially adversarial), the generator part of the GAN is used to reconstruct this input to fit the learned distribution of the original dataset.
3. Classifying Reconstructed Data: The reconstructed data, which ideally no longer contains the adversarial perturbations, is fed into the classifier for final classification.

Key Results

The paper demonstrates the efficacy of Defense-GAN through extensive experimental evaluations. The framework is tested against various adversarial attack types, including FGSM, BIM, and DeepFool, using datasets such as MNIST and CIFAR-10. Key numerical results from the experiments include:

• On the MNIST dataset, classifiers protected by Defense-GAN achieved up to a 95.7% success rate in correctly classifying adversarial examples, compared to significantly lower success rates for classifiers without such protection.
• On the CIFAR-10 dataset, there was a substantial enhancement in robustness, with a reduction in the success rate of adversarial attacks by a notable margin when Defense-GAN was applied.

Implications and Future Directions

The methodological framework of Defense-GAN reflects a significant step forward in addressing the perennial issue of adversarial attacks. By utilizing GANs, the approach demonstrates a feasible and effective way to enhance the robustness of classifiers without compromising their performance on clean data.

Theoretical implications are centered on the potential of generative models to act as a defense mechanism in adversarial settings, suggesting a new frontier for adversarial defense research. Practically, Defense-GAN can be integrated into various real-world applications, such as autonomous systems, financial fraud detection, and healthcare diagnostics, where security against adversarial attacks is paramount.

Future developments in this domain may focus on improving the efficiency of the generative process. There is also potential for exploring other generative architectures, beyond GANs, to further refine the reconstruction phase. Additionally, scaling the Defense-GAN framework to more complex datasets and deep neural network architectures remains a critical area of investigation.

In conclusion, Defense-GAN provides a promising route for fortifying machine learning models against adversarial threats and lays the groundwork for future advancements in secure and resilient AI systems.