Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression (1705.02900v1)

Abstract: Deep neural networks (DNNs) have achieved great success in solving a variety of ML problems, especially in the domain of image recognition. However, recent research showed that DNNs can be highly vulnerable to adversarially generated instances, which look seemingly normal to human observers, but completely confuse DNNs. These adversarial samples are crafted by adding small perturbations to normal, benign images. Such perturbations, while imperceptible to the human eye, are picked up by DNNs and cause them to misclassify the manipulated instances with high confidence. In this work, we explore and demonstrate how systematic JPEG compression can work as an effective pre-processing step in the classification pipeline to counter adversarial attacks and dramatically reduce their effects (e.g., Fast Gradient Sign Method, DeepFool). An important component of JPEG compression is its ability to remove high frequency signal components, inside square blocks of an image. Such an operation is equivalent to selective blurring of the image, helping remove additive perturbations. Further, we propose an ensemble-based technique that can be constructed quickly from a given well-performing DNN, and empirically show how such an ensemble that leverages JPEG compression can protect a model from multiple types of adversarial attacks, without requiring knowledge about the model.

• The paper demonstrates that JPEG compression effectively reduces adversarial noise and misclassifications in DNN image classification tasks.
• It presents an ensemble-based defense strategy by re-training models with different compression levels, enhancing network resilience.
• Empirical evaluations on CIFAR-10 and GTSRB datasets reveal that moderate compression optimally balances noise reduction and image integrity.

JPEG Compression for Adversarial Robustness in DNNs

The paper titled "Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression" explores the application of JPEG compression as a defense mechanism against adversarial attacks aimed at deep neural networks (DNNs), specifically in image classification tasks. The authors propose an innovative approach that leverages an inherently lossy image compression algorithm to mitigate the negative effects of adversarial perturbations, which have been shown to significantly compromise the performance of state-of-the-art neural network models.

Problem Context

DNNs, while demonstrating substantial advancement in image recognition, face a critical vulnerability: their susceptibility to adversarially crafted inputs. Such inputs, generated by introducing minor, often imperceptibly masked perturbations to images, exploit specific weaknesses in neural network models, leading to confident misclassifications. This vulnerability poses substantial risks, particularly for applications deployed in security-sensitive settings. Prior research has identified numerous adversarial attacks, among which the Fast Gradient Sign Method (FGSM) and DeepFool (DF) attacks are prevalent for their efficiency and subtlety.

JPEG Compression as a Defensive Mechanism

The authors propose a defensive mechanism that integrates JPEG compression as a pre-processing step in the image classification pipeline. JPEG, widely recognized for its capabilities in image storage and transmission, introduces distortion that aligns closely with the limitation of human perceptual abilities, specifically targeting high-frequency signal components in images. This characteristic allows JPEG compression to attenuate adversarial noise effectively, facilitating the restoration of correct classification outputs from adversarial instances.

Empirical Evaluation

To empirically assess their proposed method, the researchers conduct comprehensive experiments using CIFAR-10 and GTSRB datasets, demonstrating how JPEG compression, when systematically applied, mitigates the success rate of adversarial attacks (e.g., FGSM, DeepFool). Their tests reveal that even minimal compression astutely reduces adversarial influence, particularly effective in scenarios where perturbations are subtle yet destructive. One crucial discovery is that compression quality considerably affects resilience against adversarial examples; moderate compression typically offers optimal defense by balancing noise attenuation and image integrity preservation.

Ensemble-Based Defense Strategy

Beyond pre-processing, the authors introduce an ensemble-based defense strategy that synthesizes multiple JPEG-compressed model variants. This "vaccination" technique involves re-training neural network models using differently compressed image datasets to create a diverse ensemble that significantly enhances adversarial robustness. This model ensemble essentially forms an integrated defense, where adversarial perturbations fail to project across the combined decision boundaries of the ensemble, substantially reducing the attack success rate.

Implications and Future Research Directions

The deployment of JPEG compression as a pre-treatment for adversarial robustness in DNNs has meaningful implications. It provides a practical and easily applicable method that requires neither extensive model redesign nor detailed knowledge about potential attacks. By capitalizing on widely available software support for JPEG, non-experts can potentially enhance the security of image classification systems efficiently. The ensemble approach further exemplifies potential for extending model robustness, but requires careful balance between computational overhead and defensive benefits.

In ongoing and future research efforts, expansion into diverse adversarial frameworks and data domains will be pivotal in understanding the complete spectrum of JPEG's utility in AI security. Furthermore, optimizing ensemble configurations and exploring alternative compression strategies could further bolster DNN resilience against adversarial inputs. The practical integration of these methods into complex real-world applications, without degrading overall system performance, remains a challenging yet promising avenue for future exploration.