SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications (1812.02132v3)

Abstract: One major factor impeding more widespread adoption of deep neural networks (DNNs) is their lack of robustness, which is essential for safety-critical applications such as autonomous driving. This has motivated much recent work on adversarial attacks for DNNs, which mostly focus on pixel-level perturbations void of semantic meaning. In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. To do this, we re-frame the adversarial attack problem as learning a distribution of parameters that always fools the agent. In the semantic case, our proposed adversary (denoted as BBGAN) is trained to sample parameters that describe the environment with which the black-box agent interacts, such that the agent performs its dedicated task poorly in this environment. We apply BBGAN on three different tasks, primarily targeting aspects of autonomous navigation: object detection, self-driving, and autonomous UAV racing. On these tasks, BBGAN can generate failure cases that consistently fool a trained agent.

• The paper introduces SADA, a framework that uses a black-box GAN to generate both semantic and pixel-level adversarial attacks.
• It formulates attack generation as an optimization problem over environment parameters, causing high failure rates in object detection, self-driving, and UAV racing.
• The study offers a diagnostic tool to preemptively identify and mitigate vulnerabilities in safety-critical autonomous systems.

An Overview of SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

The susceptibility of deep neural networks (DNNs) to adversarial attacks is a critical concern hindering their adoption in safety-critical fields such as autonomous driving. The paper "SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications" addresses this issue by proposing a novel and comprehensive framework that includes both semantic and pixel-level adversarial attacks. Traditional adversarial attacks often involve pixel-level perturbations, limited in scope because they generally do not account for real-world semantic disruptions.

The authors, Abdullah Hamdi, Matthias Müller, and Bernard Ghanem, present the Black-Box Generative Adversarial Network (BBGAN), which is designed to generate semantic perturbations that can fool autonomous systems in diverse scenarios including object detection, self-driving, and UAV racing. Unlike traditional white-box attacks that require access to a model's parameters, BBGAN operates in a black-box manner, interacting with an agent through a simulated environment.

Methodological Innovations

The paper formulates adversarial attacks as a general optimization problem, aiming to determine a distribution of environment parameters (semantic perturbations) that consistently causes the agent to fail at its task. This approach diverges from typical adversarial attacks by focusing on environmental changes such as variations in camera viewpoints, lighting, and road layouts, which are more representative of real-world conditions.

BBGAN is utilized to model the continuous distribution of adversarial parameters, leveraging the strengths of GANs in high-dimensional spaces. This innovative use allows exploring a broad set of scenarios that are difficult to explicitly specify in advance. The methodology involves iteratively generating and refining adversarial samples, utilizing the agent's performance feedback to improve the attack generation process.

Experimental Results

The research explores three primary domains: object detection using YOLOv3, autonomous driving simulations in CARLA, and UAV racing in Sim4CV. Across these applications, BBGAN demonstrates significant effectiveness in revealing the vulnerabilities of the tested models. The adversarial samples generated during experiments led to remarkably high failure rates in the agents compared to standard methods.

For instance, in object detection tasks, even sophisticated detectors like YOLOv3 were fooled into misclassifying objects or failing to detect them when exposed to BBGAN-generated perturbations. In autonomous driving scenarios, subtle changes in environmental conditions dramatically impacted the driving policy's success rate. Similarly, in UAV racing, changes in the race course significantly reduced the UAV's ability to successfully navigate through gates.

Implications and Future Work

The implications of this research are manifold. Practically, it provides a diagnostic tool for developers to preemptively identify and mitigate failure modes in autonomous systems before real-world deployment. Theoretically, it underscores the importance of robustness and adaptability in model training by offering insights that extend beyond typical training scenarios.

Moving forward, there is scope to refine BBGAN further by improving the efficiency of its generative models, potentially incorporating more sophisticated learning algorithms. Expanding the framework to incorporate real-world data and situations could bridge the gap between simulation and reality, enhancing the practical applicability of the research.

In summary, the introduction of semantic adversarial attacks as presented in this paper marks a critical step toward safer and more reliable autonomous systems. As AI continues to evolve, ensuring robustness against real-world adversarial conditions remains an essential objective, highlighting the value of research that explores vulnerabilities from all dimensions, not just the pixel-level.