Adversarial camera stickers: A physical camera-based attack on deep learning systems (1904.00759v4)

Abstract: Recent work has documented the susceptibility of deep learning systems to adversarial examples, but most such attacks directly manipulate the digital input to a classifier. Although a smaller line of work considers physical adversarial attacks, in all cases these involve manipulating the object of interest, e.g., putting a physical sticker on an object to misclassify it, or manufacturing an object specifically intended to be misclassified. In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself? We show that by placing a carefully crafted and mainly-translucent sticker over the lens of a camera, one can create universal perturbations of the observed images that are inconspicuous, yet misclassify target objects as a different (targeted) class. To accomplish this, we propose an iterative procedure for both updating the attack perturbation (to make it adversarial for a given classifier), and the threat model itself (to ensure it is physically realizable). For example, we show that we can achieve physically-realizable attacks that fool ImageNet classifiers in a targeted fashion 49.6% of the time. This presents a new class of physically-realizable threat models to consider in the context of adversarially robust machine learning. Our demo video can be viewed at: https://youtu.be/wUVmL33Fx54

Adversarial Camera Stickers: A Novel Physical Attack on Deep Learning Systems

The paper, "Adversarial camera stickers: A physical camera-based attack on deep learning systems," presents a novel approach in the domain of adversarial attacks on machine learning models. Whereas traditional adversarial attacks predominantly operate within a digital framework, manipulating pixel values directly to mislead classifiers, this paper investigates attacks that rely on physical alterations not to the object itself, but to the imaging process. This investigation aligns with ongoing efforts to assess the vulnerabilities of deep learning models in real-world applications.

Overview and Methodology

The authors introduce an adversarial camera sticker—a mostly translucent sticker affixed to a camera lens—that falsifies image classifications universally across all instances of a target class. The attack employs a carefully designed pattern of dots, optimized to create adversarial changes in the images captured by the camera. Unlike previous physical attacks that alter the object being photographed, the proposed method impacts the optics, presenting an inconspicuous but effective perturbation path.

Central to the approach is an iterative procedure that adjusts the sticker pattern to ensure it remains adversarial across different conditions and physically realizable through the camera lens. The paper discusses an alpha blending technique that models how light through a sticker changes the observed image, enabling controlled perturbations. The authors optimized the parameters of this translucent dot pattern using a combination of printed trials and simulations, achieving a successful targeted misclassification rate of approximately 49.6% with certain object categories on the ImageNet dataset.

Experimental Results

The research demonstrates the efficacy of adversarial camera stickers through both simulated and real-world experiments. In controlled tests using ImageNet images, the attack significantly reduced classification accuracy for chosen categories, and in practical scenarios, it maintained a high fooling rate across various viewing angles and distances. These results underscore the threat posed by such physical attacks, particularly in security-sensitive applications where camera feeds are integral.

Specifically, the research discusses the fooling rate of crafted adversarial perturbations in settings involving computer keyboards misclassified as computer mice, as well as street signs misclassified as guitar picks. These transformations highlight potential areas of vulnerability that could be exploited in autonomous vehicles and surveillance systems, where object recognition reliability is crucial.

Implications and Future Directions

The paper's contribution is manifold. Firstly, it advances the understanding of physical adversarial attacks, a less-explored yet critical area within machine learning security. By expanding adversarial attack methodologies to the camera optics, the paper opens new vectors of potential exploitation that must be considered during the deployment of AI technologies.

Secondly, the approach presents a new suite of challenges for developing robust machine learning models. The adversarial stickers exemplify how constrained physical modifications can result in significant misclassification errors, suggesting a need for improved model robustness against both digital and physical adversarial tactics.

Going forward, research following this direction could address the complexity augmentation of adversarial patterns, potentially enhancing their real-world applicability by incorporating dynamic environmental factors such as lighting and motion. Additionally, the creation of real-time defenses against such optical distortions will be a critical area of focus, especially in applications involving security and safety.

In summary, this paper contributes significantly to adversarial machine learning by exploring a novel and pragmatic attack method. It prompts the research community to reconsider the implications of adversarial perturbations beyond the digital domain and challenges future work to devise more sophisticated defenses against such innovative attacks.