[Paper accepted at ACNS 2020]

In this paper, we propose a simple and effective attack on the recently introduced Smartphone Authentication with Built-in Camera Protocol, called ABC. The ABC protocol uses the photo-response non-uniformity (PRNU) as the main authentication factor in combination with anti-forgery detection systems. The ABC protocol interprets the PRNU as a fingerprint of the camera sensor built-in a smartphone device. The protocol works as follows: during the authentication process, the user is challenged with two QR codes (sent by the server) that need to be photographed with a pre-registered device. In each QR code, the server embeds a unique pattern noise (not visible to the naked eye), called probe signal, that is used to identify potential forgeries. The inserted probe signal is very similar to a genuine fingerprint. The photos of QR codes taken by the user are then sent to the server for verification. The server checks (i) if the photos contain the user’s camera fingerprint (used to authenticate the pre-registered device) and (ii) if the photos contain the embedded probe signal. If an adversary tries to remove (subtract) his own camera fingerprint and replace it with the victim’s camera fingerprint (computed from photos shared on social media), then he will implicitly remove the embedded probe signal and the attack will fail. The ABC protocol is able to detect these attacks with a false acceptance rate (FAR) of 0.5%. However, the ABC protocol wrongly assumes that the attacker can only determine his own camera fingerprint from the photos of the presented QR codes. The attack proposed in our work is able to get past the anti-forgery detection system with a FAR of 54.1%, simply by estimating the attacker’s camera fingerprint from a different set of photos (e.g. five photos) owned by the attacker. This set of photos can be trivially obtained before the attack, allowing the adversary to compute his camera fingerprint independently of the attack. The key to the success of our attack is that the independently computed adversary’s camera fingerprint does not contain the probe signal embedded in the QR codes. Therefore, when we subtract the adversary’s camera fingerprint and add the victim’s camera fingerprint, the embedded probe signal will remain in place. For this reason, the proposed attack can successfully pass through the anti-forgery detection system of the ABC protocol. In this paper, we also propose a potential fix based on analyzing signals from built-in motion sensors, which are not typically shared on social media.