TY - JOUR
T1 - Detecting multiple H.264/AVC compressions with the same quantisation parameters
AU - Zhang, Zhenzhen
AU - Hou, Jianjun
AU - Zhang, Yu
AU - Ye, Jingyu
AU - Shi, Yunqing
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2016.
PY - 2017/5/1
Y1 - 2017/5/1
N2 - Multiple-compression detection is of particular importance in video forensics, as it reveals possible manipulations to the content. However, methods for detecting multiple compressions with same quantisation parameters (QPs) are rarely reported. To deal with this issue, a novel method is presented in this study to detect multiple H.264/advanced video coding compressions with the same QPs. First, a new set, named ratio difference set (RDS), is proposed, which is calculated by identifying the quantised DCT coefficients whose values will be changed after re-compression. Then, a discriminative and fixed statistical feature set extracted from RDS of each video is obtained to serve as input for classification. With the aid of support vector machines, the extracted feature set is used to classify the videos that have undergone H.264 compressions twice or more from those compressed just once. Experimental results show that high classification accuracy and robustness against copy-move attack and frame-deletion attack can be achieved with the authors' proposed method.
AB - Multiple-compression detection is of particular importance in video forensics, as it reveals possible manipulations to the content. However, methods for detecting multiple compressions with same quantisation parameters (QPs) are rarely reported. To deal with this issue, a novel method is presented in this study to detect multiple H.264/advanced video coding compressions with the same QPs. First, a new set, named ratio difference set (RDS), is proposed, which is calculated by identifying the quantised DCT coefficients whose values will be changed after re-compression. Then, a discriminative and fixed statistical feature set extracted from RDS of each video is obtained to serve as input for classification. With the aid of support vector machines, the extracted feature set is used to classify the videos that have undergone H.264 compressions twice or more from those compressed just once. Experimental results show that high classification accuracy and robustness against copy-move attack and frame-deletion attack can be achieved with the authors' proposed method.
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U2 - 10.1049/iet-ifs.2015.0361
DO - 10.1049/iet-ifs.2015.0361
M3 - Article
AN - SCOPUS:85018160599
SN - 1751-8709
VL - 11
SP - 152
EP - 158
JO - IET Information Security
JF - IET Information Security
IS - 3
ER -