Title :
Exposing video forgeries by detecting MPEG double compression
Author :
Sun, Tanfeng ; Wang, Wan ; Jiang, Xinghao
Author_Institution :
Sch. of Electron. Inf. & Electr. Eng., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
In this paper, an improved video tampering detection model based on MPEG double compression is proposed. Double compression will import disturbance into Discrete Cosine Transform (DCT) coefficients, reflecting in the violation of the parametric logarithmic law for first digit distribution of quantized Alternating Current (AC) coefficients. A 12-D feature can be extracted from each group of pictures (GOP) and machine learning framework is adopted to enhance the detection accuracy. Furthermore, a novel approach with a serial Support Vector Machine (SVM) architecture to estimate original bit rate scale in doubly compressed video is proposed. Experiments demonstrate higher accuracy and effectiveness.
Keywords :
data compression; discrete cosine transforms; feature extraction; learning (artificial intelligence); object detection; quantisation (signal); security of data; support vector machines; video coding; 12-D feature extraction; AC coefficient; DCT coefficient; GOP; MPEG double compression detection; SVM architecture; bit rate scale; discrete cosine transform; doubly compressed video; first digit distribution; group of pictures; machine learning; parametric logarithmic law; quantized alternating current; support vector machine; video forgeries; video tampering detection model; Accuracy; Bit rate; Discrete cosine transforms; Encoding; Feature extraction; Support vector machines; Transform coding; Video forgery; double compression; first digit distribution; machine learning;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4673-0045-2
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2012.6288150
