Generating Natural Adversarial Examples (1710.11342v2)

Abstract: Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers.

• The paper introduces a GAN-based framework that generates natural adversarial examples by exploring the semantic latent space.
• It employs a generator and an inverter to map data between continuous latent vectors and realistic inputs, ensuring semantic coherence.
• The methodology enhances model vulnerability assessments with applications in image classification and textual processing tasks.

Generating Natural Adversarial Examples: An Expert Review

The paper "Generating Natural Adversarial Examples" by Zhao, Dua, and Singh addresses the pressing issue of adversarial vulnerability in machine learning models. Traditional adversarial examples involve minor perturbations to inputs that can lead to significant errors in model predictions. However, these perturbations often produce inputs that lack semantic meaning, making them less applicable in complex domains like language.

Framework and Methodology

This work introduces a novel framework that generates adversarial examples by searching in the semantic space of dense, continuous data representations. The approach leverages Generative Adversarial Networks (GANs) to achieve this. By mapping the data through a latent space, the authors create adversarial instances that are both natural and meaningful.

The framework involves two key components: a generator and an inverter. The generator learns a mapping from normally distributed latent vectors to data instances, creating realistic adversarial samples. The inverter understands the mapping of real data instances back to this latent space, facilitating the exploration of adversarial examples in this semantic space.

Applications and Results

The framework is applied across various tasks, demonstrating its wide applicability:

• Image Classification: Experiments on datasets like MNIST and LSUN showcase the generation of natural-looking adversarial images that maintain semantic coherence. This contrasts with traditional approaches like FGSM, which produce noise-heavy and less interpretable adversaries.
• Textual Domains: The proposal is extended to language processing, wherein adversaries are generated for tasks such as textual entailment and machine translation. This capability stems from using an adversarially regularized autoencoder to handle discrete data, ensuring syntactic correctness in generated sentences.

The paper reports significant outcomes in terms of the naturalness and legibility of adversarial examples, verified through a combination of quantitative measures (e.g., perturbation distances in latent space) and human evaluations.

Implications and Future Directions

This research has substantial implications for understanding and improving model robustness. By generating adversarial examples that retain semantic fidelity, it opens avenues for more insightful vulnerability assessments and robustness enhancements of machine learning models. The technique can be extended to evaluate black-box models without requiring gradient access, making it relevant for practical applications where models often operate in opaque environments.

Future developments could explore more sophisticated search algorithms and the integration of alternative generative models like VAEs. Advances in GAN training methodologies and their incorporation into this framework could further enhance the quality of adversarial samples.

In conclusion, this paper provides an interdisciplinary approach combining adversarial learning and generative modeling to address crucial shortcomings in adversarial example generation, particularly within complex domains requiring semantic coherence. The results contribute toward more resilient machine learning systems, effectively bridging theoretical advancements with practical necessities.