Game theoretic analysis of camera source identification

Sensor pattern noise (SPN) is recognized as a reliable device fingerprint for camera source identification (CSI). However, source identification method (source test) ignores whether the fingerprint is forged and anti-forensic techniques seldom consider traces they leave behind. Therefore, the performance of above techniques needs to be evaluated again by assuming the existence of both parties of a forensic investigator and an anti-forensic forger. In this paper, we propose a novel counter anti-forensic method based on noise level estimation to detect the possible forgery (forgery test). Furthermore, we evaluate the Nash equilibrium performance when investigator performs both source test and forgery test, and identify the optimal strategies of both parties with the game theory. The experimental results show that our proposed method can achieve good performance without collecting the candidate image set in the existing triangle test method especially when the false alarm rate is held low (e.g. P_fa <; 5%).