Adversarial examples for generative models (1702.06832v1)

Abstract: We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.

• The paper presents three novel adversarial attack methods—classifier-based, VAE loss-based, and latent space attacks—to compromise deep generative models.
• It demonstrates that the latent space attack consistently produces high-fidelity reconstructions with minimal perturbations across diverse datasets.
• The study highlights security risks in generative models and advocates for developing robust defenses and resilient network architectures.

Adversarial Examples for Generative Models: An Expert Synthesis

The paper "Adversarial examples for generative models" by Jernej Kos, Ian Fischer, and Dawn Song provides a detailed exploration of targeted adversarial attacks on deep generative models, specifically the Variational Autoencoder (VAE) and the VAE-GAN (Variational Autoencoder paired with a Generative Adversarial Network). While adversarial examples have been extensively studied in the domain of classification networks, this work extends that knowledge to the field of generative models, demonstrating their susceptibility to adversarial manipulations.

Core Contributions

The authors propose and evaluate three novel methods for generating adversarial attacks against VAE and VAE-GAN architectures. These attacks are evaluated on datasets such as MNIST, SVHN, and CelebA. The contributions can be summarized in three primary attack strategies:

1. Classifier-based Attack: By integrating a classifier into the trained encoder of the target generative model, this approach leverages existing methods of adversarial example generation developed for classification networks. This attack exploits the classifier to find adversarial perturbations that are subsequently utilized to manipulate the latent representation of the generative model. Despite the innovative integration, the experiments suggest that incorporating a classifier introduces irregularities, leading to lower quality reconstructions compared to direct latent attacks.
2. VAE Loss-based Attack: This method adjusts the VAE's loss function during adversarial generation. By computing reconstruction loss directly between adversarial and target reconstructions, the approach efficiently circumvents the limitations of classifier-based attacks. While effective on clean datasets like MNIST, the performance varied with more complex datasets.
3. Latent Space Attack: The most direct approach targets differences in latent space representations. By optimizing for minimal deviation in latent vectors, this method inherently provides robust adversarial examples suitable for constructing plausible yet misrepresented outputs. Notably, this attack yielded superior performance compared to the others across different datasets, particularly on more intricate datasets like SVHN.

Experimental Findings

Significantly, the paper highlights that the best-performing method is the direct latent attack, which consistently generates adversarial examples with smaller perturbations and higher fidelity reconstructions than the other methods. The researchers observed that exploiting the latent space does not only result in targeted adversarial examples but also bypasses the constraints inherent in classification attacks. The authors further underscore that the latent attack is computationally efficient, offering fast adversary generation without necessitating recursive reconstruction during optimization.

Implications and Speculation on Future Developments

The findings suggest several practical and theoretical implications for the field of adversarial machine learning in generative models:

• Practical Implications: The vulnerabilities exposed indicate critical risks for applications leveraging generative models. These include tasks in image compression, data privacy management, and potentially countless AI-driven services reliant on generatively modeled data.
• Theoretical Implications: On a theoretical plane, the results invigorate a broader understanding that adversarial attacks transcend beyond classification boundaries, asserting themselves as a generalized phenomenon within deep learning architectures.
• Future Directions in AI: The authors candidly propose assessing defenses against such adversarial strategies as a promising pathway. Future research should aim to engineer more resilient network architectures and preventive methodologies against these sophisticated attacks. Furthermore, extending adversarial methodologies to handle generative models trained on natural and sophisticated image datasets such as CIFAR-10 and ImageNet poses an expansive research territory.

Conclusion

This paper stands as a pivotal work bridging the gap between adversarial strategies in classification networks and their application in generative models. By articulating novel adversary methods and insights, the researchers lay groundwork towards a deeper understanding of generative model vulnerabilities. As AI continues to evolve, addressing these susceptibilities remains imperative, ensuring more secure deployment of generative models across diverse applications.