In this paper, we present video-based cryptanalysis, a new method used to recover secret keys from a device by analyzing video footage of a device’s power LED. We show that cryptographic computations performed by the CPU change the power consumption of the device which affects the brightness of the device’s power LED. Based on this observation, we demonstrate how attackers can exploit commercial video cameras (e.g., an iPhone 13’s camera or Internet-connected security camera) to recover secret keys from devices. This is done by obtaining video footage of a device’s power LED (in which the frame is filled with the power LED) and exploiting the video camera’s rolling shutter to increase the sampling rate by three orders of magnitude from the frames per second (FPS) rate (60 measurements per second) to the rolling shutter speed (60K measurements per second in the iPhone 13 Pro Max). The frames of the video footage of the device’s power LED are analyzed in the RGB space, and the associated RGB values are used to recover the secret key by inferring the device’s power consumption from the RGB values. We demonstrate the application of video-based cryptanalysis by performing two side-channel cryptanalytic timing attacks and recover: (1) a 256-bit ECDSA key from a smart card by analyzing video footage of the power LED of a smart card reader obtained by a hijacked Internet-connected security camera located 16 meters away from the smart card reader, and (2) a 378-bit SIKE key from a Samsung Galaxy S8 by analyzing video footage of the power LED of Logitech Z120 USB speakers that were connected to the same USB hub used to charge the Galaxy S8 obtained by an iPhone 13 Pro Max’s camera. We also discuss countermeasures, limitations, and the future of video-based cryptanalysis in light of the expected improvements in video camera specifications.

Although power LEDs have been integrated in various devices that perform cryptographic operations for decades, the cryptanalysis risk they pose has not yet been investigated. In this paper, we present optical cryptanalysis, a new form of cryptanalytic side-channel attack, in which secret keys are extracted by using a photodiode to measure the light emitted by a device's power LED and analyzing subtle fluctuations in the light intensity during cryptographic operations. We analyze the optical leakage of power LEDs of various consumer devices and the factors that affect the optical SNR. We then demonstrate end-to-end optical cryptanalytic attacks against a range of consumer devices (smartphone, smartcard, and Raspberry Pi, along with their USB peripherals) and recover secret keys (RSA, ECDSA, SIKE) from prior and recent versions of popular cryptographic libraries (GnuPG, Libgcrypt, PQCrypto-SIDH) from a maximum distance of 25 meters.

In this paper, we investigate "split-second phantom attacks," a scientific gap that causes two commercial advanced driver-assistance systems (ADASs), Telsa Model X (HW 2.5 and HW 3) and Mobileye 630, to treat a depthless object that appears for a few milliseconds as a real obstacle/object. We discuss the challenge that split-second phantom attacks create for ADASs. We demonstrate how attackers can apply split-second phantom attacks remotely by embedding phantom road signs into an advertisement presented on a digital billboard which causes Tesla's autopilot to suddenly stop the car in the middle of a road and Mobileye 630 to issue false notifications. We also demonstrate how attackers can use a projector in order to cause Tesla's autopilot to apply the brakes in response to a phantom of a pedestrian that was projected on the road and Mobileye 630 to issue false notifications in response to a projected road sign. To counter this threat, we propose a countermeasure which can determine whether a detected object is a phantom or real using just the camera sensor. The countermeasure (GhostBusters) uses a "committee of experts" approach and combines the results obtained from four lightweight deep convolutional neural networks that assess the authenticity of an object based on the object's light, context, surface, and depth. We demonstrate our countermeasure's effectiveness (it obtains a TPR of 0.994 with an FPR of zero) and test its robustness to adversarial machine learning attacks.