Inter-frame Tamper Forensic Algorithm Based on Structural Similarity Mean Value and Support Vector Machine

Abstract

With the development of network technology and multimedia technology, digital video is widely used in news, business, finance, and even appear in court as evidence. However, digital video editing software makes it easier to tamper with video. Digital video tamper detection has become a problem that video evidence must solve. Aiming at the common inter-frame tampering in video tampering, a tampered video detection method based on structural similarity mean value and support vector machine is proposed. First, the structural similarity mean value feature of the video to be detected is extracted, which has good classification characteristics for the original video and the tampered video. Then, the structural similarity mean value is input to the support vector machine, and the tampered video detection is implemented by using the good non-linear classification ability of the support vector machine. The comparison simulation results show that the detection performance of this method for tampered video is better than that based on optical flow characteristics.