Researchers find heat sources can trick thermal cameras in drones and autonomous vehicles, causing them to miss obstacles.
Thermal cameras help drones and autonomous vehicles detect obstacles when visibility is poor. But new research shows these sensors can be tricked into missing real objects or detecting ones that are not actually there.
Researchers at the University of Florida have identified three vulnerabilities in thermal cameras that can manipulate how machines interpret heat signatures. The flaws could cause drones, robots, or autonomous vehicles to misread their surroundings, potentially undermining safety-critical systems such as obstacle detection.Unlike traditional cyberattacks, the researchers found that these vulnerabilities do not require hacking the device. Environmental heat sources alone can trigger them, altering how the camera processes temperature data.The team found that certain heat patterns can interfere with the camera’s internal signal processing, making objects appear colder, warmer, or even invisible to the perception system.Thermal cameras fooledThe study focuses on weaknesses in three key areas of thermal camera operation: image equalization, sensor calibration, and lens behavior. Together, these processes convert raw thermal signals into the images that machines rely on to interpret the environment.By exploiting these internal mechanisms, attackers or even naturally occurring heat sources could distort the camera’s output. In some cases, the system might hide a real obstacle such as a person or object. In others, it could generate phantom obstacles that do not actually exist.These effects occur because the manipulation happens inside the sensor’s processing pipeline rather than by altering the final image.“Everything that we discovered is internal to the sensor, so the data are pretty much already manipulated when they are used by the drone or the car,” said Sara Rampazzi, an assistant professor in the university’s Department of Computer and Information Science and Engineering.Rampazzi’s research group evaluated existing algorithms used by thermal camera manufacturers and found they could be influenced by certain heat patterns in the environment.“We evaluate state-of-the-art algorithms and software running inside the cameras that are deployed by the manufacturers, and we’re basically saying that they need to be safer,” she said.Real-time defenses developedTo address the problem, the researchers also created defensive signal-processing techniques designed to detect misleading thermal signatures in real time.Their system analyzes incoming sensor readings and flags patterns that are likely produced by suspicious heat sources. Once detected, those readings can be excluded from the perception pipeline before they affect the drone or robot’s decision-making.The team tested the method using large datasets of thermal images and simulated attack scenarios. These experiments helped them understand how the vulnerabilities behave under different environmental conditions.Running these simulations required substantial computing power. Researchers relied on the university’s HiPerGator supercomputer to process large batches of experiments and analyze how perception models respond to manipulated heat signals.“HiPerGator’s parallel processing capabilities enabled us to efficiently run large batches of experiments, simulate diverse attack scenarios and analyze model behavior at scale,” Rampazzi said.Beyond identifying vulnerabilities, the team said it is working with manufacturers to ensure future systems are more resilient.The research was presented at the Network and Distributed System Security Symposium.
Computer Vision Cybersecurity Drones Heat Sources Robotics Safety Sensor Vulnerabilities Thermal Cameras
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