Polar codes have promising error-correction capabilities. Yet, decoding polar codes is often challenging, particularly with large blocks, with recently proposed decoders based on list-decoding or neural-decoding. The former applies multiple decoders, while the latter family learns to decode from data. In this work we introduce a novel polar decoder that combines list-decoding with neural-decoding, by forming an ensemble of multiple weighted belief-propagation (BP) decoders trained with different data. We employ the cyclic-redundancy check code as a proxy for combining the ensemble decoders and selecting the most-likely decoded word after inference, while facilitating real-time decoding. We evaluate our decoder over a wide range of polar codes lengths, empirically showing gains of around 0.25dB in frame-error rate. Our complexity and latency analysis shows that the number of operations approaches that of a single BP decoder at high SNR.

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.