# GhostImage: Remote Perception Attacks against Camera-based Image Classification Systems Source: [GhostImage: Remote Perception Attacks against Camera-based Image Classification Systems]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:22:05.587Z Authors: Yanmao Man, Ming Li, Ryan Gerdes Published: 2020-01-21T21:58:45.000Z ## Abstract In vision-based object classification systems imaging sensors perceive the environment and machine learning is then used to detect and classify objects for decision-making purposes; e.g., to maneuver an automated vehicle around an obstacle or to raise an alarm to indicate the presence of an intruder in surveillance settings. In this work we demonstrate how the perception domain can be remotely and unobtrusively exploited to enable an attacker to create spurious objects or alter an existing object. An automated system relying on a detection/classification framework subject to our attack could be made to undertake actions with catastrophic results due to attacker-induced misperception. We focus on camera-based systems and show that it is possible to remotely project adversarial patterns into camera systems by exploiting two common effects in optical imaging systems, viz., lens flare/ghost effects and auto-exposure control. To improve the robustness of the attack to channel effects, we generate optimal patterns by integrating adversarial machine learning techniques with a trained end-to-end channel model. We experimentally demonstrate our attacks using a low-cost projector, on three different image datasets, in indoor and outdoor environments, and with three different cameras. Experimental results show that, depending on the projector-camera distance, attack success rates can reach as high as 100% and under targeted conditions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()