Universal Adversarial Perturbations Against Semantic Image Segmentation (1704.05712v3)

Abstract: While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exist universal perturbations that are input-agnostic but fool the network on the majority of inputs. While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial perturbations that make the network yield a desired target segmentation as output. We show empirically that there exist barely perceptible universal noise patterns which result in nearly the same predicted segmentation for arbitrary inputs. Furthermore, we also show the existence of universal noise which removes a target class (e.g., all pedestrians) from the segmentation while leaving the segmentation mostly unchanged otherwise.

Analysis of "Universal Adversarial Perturbations Against Semantic Image Segmentation"

The paper "Universal Adversarial Perturbations Against Semantic Image Segmentation" explores the vulnerabilities of deep learning models, specifically in the context of semantic image segmentation, against adversarial attacks. These attacks involve creating small perturbations to input images that lead models to produce incorrect outputs while remaining mostly imperceptible to humans.

Overview

The authors extend the concept of universal adversarial perturbations, previously applied primarily to image classification tasks, to semantic image segmentation. Semantic segmentation consists of labeling each pixel in an image with a class, which is vital for applications such as autonomous driving and video surveillance. In this context, adversarial attacks are particularly concerning as they could lead to severe consequences if deployed in real-world environments.

Methodology

The authors present a method to generate universal perturbations that are input-agnostic. This allows the perturbation to affect a wide range of inputs similarly, making them exceedingly potent and potentially damaging if applied in practical applications. The authors explore two main types of adversarial targets for these perturbations: static and dynamic.

1. Static Target Segmentation: This involves designing perturbations such that for any input image, the network predicts a fixed, static segmentation map.
2. Dynamic Target Segmentation: Here, the aim is to remove specific target classes (like "person") from all images while maintaining the rest of the segmentation map consistent with the network's original predictions.

The authors utilize an iterative method analogous to the least-likely method (LLM) but adapted for semantic segmentation. They optimize these universal perturbations on a training dataset and demonstrate generalization capabilities on unseen validation images.

Empirical Evaluations

Key empirical findings include:

• The presence of universal adversarial perturbations that can consistently manipulate the outputs of semantic segmentation networks across different inputs.
• The generated perturbations maintain a high attack success rate on both training and validation data, indicating their broad applicability and lack of overfitting.
• The structured nature of the perturbations, reflecting distinct patterns correlating to target segmentation outputs.

Quantitatively, for various settings, the perturbations achieve up to 100% accuracy in achieving their adversarial goals on training data, with similar efficacy observed in validation sets.

Implications

The research highlights the intrinsic vulnerability of deep learning models to crafted input noise, emphasizing the need for robust defense mechanisms. The success of these perturbations across different datasets also suggests their transferable nature, extending potential threats beyond specific model boundaries. From a theoretical perspective, the work underscores the challenges in securing neural networks, particularly in applications involving dense contextual information such as semantic segmentation.

Future Directions

As follow-ups to this research, exploring methods for increasing the resistance of deep networks to adversarial noise presents a critical path. Furthermore, improvements in detecting such perturbations in real-time could mitigate potential malicious uses. Extending the paper of adversarial robustness to other related tasks, such as object detection and instance segmentation, could further broaden the understanding of adversarial threats in computer vision.

Finally, the practicality of deploying adversarial attacks in the physical world remains an essential concern. Research efforts should focus on whether these perturbations can maintain their adversarial properties under varying real-world conditions, such as changes in lighting or viewing angle, to evaluate their real-world applicability genuinely.

Conclusion

This paper provides a comprehensive analysis of the vulnerabilities of semantic segmentation models to universal adversarial perturbations. The results underline both the strength of modern adversarial attacks and the pressing need for fortified AI systems, especially as these technologies are increasingly integrated into critical applications like autonomous driving.