Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink (2103.06504v1)

Abstract: Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario. In this work, we show by simply using a laser beam that DNNs are easily fooled. To this end, we propose a novel attack method called Adversarial Laser Beam ($AdvLB$), which enables manipulation of laser beam's physical parameters to perform adversarial attack. Experiments demonstrate the effectiveness of our proposed approach in both digital- and physical-settings. We further empirically analyze the evaluation results and reveal that the proposed laser beam attack may lead to some interesting prediction errors of the state-of-the-art DNNs. We envisage that the proposed $AdvLB$ method enriches the current family of adversarial attacks and builds the foundation for future robustness studies for light.

• The paper introduces AdvLB, a novel laser-based adversarial technique that deceives DNNs through precise manipulation of laser parameters.
• It employs a greedy search with k-random-restart to optimize laser parameters, achieving 95.1% success in digital simulations and 100% in indoor experiments.
• The study exposes significant security risks for DNNs and suggests defenses like randomized laser perturbation during training to improve model robustness.

An In-Depth Analysis of Adversarial Laser Beam Attacks on Deep Neural Networks

The paper "Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink" provides an exhaustive paper on exploiting laser beams as adversarial perturbations to deceive Deep Neural Networks (DNNs) under both digital and physical scenarios. This research introduces Adversarial Laser Beam ($AdvLB$) as a novel attack mechanism, extending the traditional boundaries of adversarial attacks into the physical world with simple implementation using readily available devices like laser pointers.

Overview and Methodology

The authors address a gap in existing literature by focusing on light beams, specifically, laser beams, as adversarial tools. While prior studies have dealt with digital perturbations or physical attacks using stickers and projections, the exploration of coherent light sources like lasers presents a new dimension in attack strategies. The $AdvLB$ approach manipulates laser beam parameters such as wavelength, layout, width, and intensity to fool state-of-the-art DNN models. By introducing adversarial perturbations into images captured by cameras, this method can alter the model's inference, resulting in significant misclassifications without the necessity for direct access to the target model.

The optimization process for finding effective laser parameters is conducted using a greedy search mechanism followed by a $k$-random-restart strategy to circumvent local optima challenges. The optimization's objective is to minimize the confidence in the true class provided by the target model until a misclassification occurs.

Experimental Evaluation

Empirical results demonstrated the efficacy of the $AdvLB$ method with a success rate of 95.1% across 1000 ImageNet test samples under digital simulations. In physical settings, $AdvLB$ achieved a 100% success rate in controlled indoor environments and 77.43% in outdoor scenarios, showcasing its real-world applicability.

The experiments also delve into the nuanced effects of various parameters on the attack's success, indicating that parameters like wavelength and beam width significantly impact the adversarial capability. For instance, certain wavelengths correlated with color perception changes that lead to misclassification, highlighting a complex interaction between DNN feature extraction and physical attributes altered by laser beams.

Implications and Future Directions

The implications of this paper are multifaceted. Practically, it raises security concerns for autonomous systems, especially in environments where lighting conditions can be easily manipulated. Theoretically, it provides insights into the DNN vulnerability spectrum and invites further research on defending against such attacks. Specifically, injecting random laser beam perturbations during the training phase improved model robustness without deteriorating its performance on clean data, suggesting a potential defense pathway.

Moving forward, the research opens several avenues: refining $AdvLB$ to perform under dynamic conditions, exploring other light-based perturbations beyond lasers, and extending research to different tasks such as object detection or segmentation. Additionally, development of robust defense mechanisms tailored for light-based adversarial attacks remains a critical area for future exploration.

In conclusion, "Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink" contributes significantly to the understanding of physical adversarial attacks, providing a foundation for ongoing advancements in the robustness of neural network models against real-world threats.