Xinghao Jiang

Exposing video forgeries by detecting MPEG double compression

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.

Detection of Double Compression in MPEG-4 Videos Based on Markov Statistics

With the spread of powerful and easy-to-use video editing software, digital videos are exposed to various forms of tampering. Nowadays, a considerable proportion of surveillance systems and video cameras have built-in MPEG-4 codec. Therefore, the detection of double compression in MPEG-4 videos as a first step in video forensics research is of significance. In this paper, Markov based features are adopted to detect double compression artifacts, which imply that the original video may have been interpolated. The advantages and limitations of double MPEG-4 compression detection are analyzed. Experimental results have demonstrated that our scheme outperforms most existing methods.

A novel video inter-frame forgery model detection scheme based on optical flow consistency

In this paper, a novel video inter-frame forgery detection scheme based on optical flow consistency is proposed. It is based on the finding that inter-frame forgery will disturb the optical flow consistency. This paper noticed the subtle difference between frame insertion and deletion, and proposed different detection schemes for them. A window based rough detection method and binary searching scheme are proposed to detect frame insertion forgery. Frame-to-frame optical flows and double adaptive thresholds are applied to detect frame deletion forgery. This paper not only detects video forgery, but also identifies the forgery model. Experiments show that our scheme achieves a good performance in identifying frame insertion and deletion model.

Exposing video inter-frame forgery based on velocity field consistency

In recent years, video forensics has become an important issue. Video inter-frame forgery detection is a significant branch of forensics. In this paper, a new algorithm based on the consistency of velocity field is proposed to detect video inter-frame forgery (i.e., consecutive frame deletion and consecutive frame duplication). The generalized extreme studentized deviate (ESD) test is applied to identify the forgery types and locate the manipulated positions in forged videos. Experiments show the effectiveness of our algorithm.

An adaptive video shot segmentation scheme based on dual-detection model

Efficient segmentation of the video shots is the important and foundational work for the research of video content retrieval and analysis. A video shot segmentation scheme based on a dual-detection model is proposed, which includes the pre-detection and re-detection processes. The concepts of uneven blocked color histogram difference and uneven blocked pixel value difference based on human visual features are introduced, which are used as the main descriptors of the pre-detection process to enlarge the importance of central areas and to reduce the noises of background movements and logos. And the adaptive binary search method is introduced to fast detect boundaries with a time complexity of O(log(2)n). In the re-detection round, the scale invariant feature transform is applied to re-detect boundaries so as to improve the detection precision rate. Experiments show that this algorithm can improve both the recall rate and the precision rate of video shot boundary detection, especially for gradual shot boundaries

Double compression detection based on local motion vector field analysis in static-background videos

A novel double compression detection method in static-background videos is proposed.Local motion vector field analysis is used to obtain segmentation of video contents.Modification based on local motion strengths is used to extract robust fingerprints.The proposed method has more robust performance than several state-of-the-art methods. Videos captured by stationary cameras are widely used in video surveillance and video conference. This kind of video often has static or gradually changed background. By analyzing the properties of static-background videos, this work presents a novel approach to detect double MPEG-4 compression based on local motion vector field analysis in static-background videos. For a given suspicious video, the local motion vector field is used to segment background regions in each frame. According to the segmentation of backgrounds and the motion strength of foregrounds, the modified prediction residual sequence is calculated, which retains robust fingerprints of double compression. After post-processing, the detection and GOP estimation results are obtained by applying the temporal periodic analysis method to the final feature sequence. Experimental results have demonstrated better robustness and efficiency of the proposed method in comparison to several state-of-the-art methods. Besides, the proposed method is more robust to various rate control modes.

Identifying Video Forgery Process Using Optical Flow

With the extensive equipment of surveillance systems, the assessment of the integrity of surveillance videos is of vital importance. In this paper, an algorithm based on optical flow and anomaly detection is proposed to authenticate digital videos and further identify the inter-frame forgery process (i.e. frame deletion, insertion, and duplication). This method relies on the fact that forgery operation will introduce discontinuity points to the optical flow variation sequence and these points show different characteristics depending on the type of forgery. The anomaly detection scheme is adopted to distinguish the discontinuity points. Experiments were performed on several real-world surveillance videos delicately forged by volunteers. The results show that the proposed algorithm is effective to identify forgery process with localization, and is robust to some degree of MPEG compression.

Detection of double compression in MPEG-4 videos based on block artifact measurement ☆

Abstract In this paper, a novel double MPEG-4 compression detection method is proposed based on block artifact measurement. According to the properties of recompressed frames, an adaptive post-filtering technique is used to measure the strength of block artifacts in decompression domain. Then, the measurement of block artifacts is combined with the Variation of Prediction Footprint (VPF) using an adjustive parameter for each frame. This combination aims to enhance the robustness of the measurement sequence against degradations caused by lossy recompression. Finally, a temporal periodic analysis method is applied to the measurement sequence to detect double compression and estimate the size of the original Group of Pictures (GOP). The performance of the proposed method is evaluated on several public-available standard videos compared with several existing algorithms. Experimental results demonstrate the proposed method has better detection capability and estimates original GOP structures more accurately. Besides, the proposed method is more robust against different transcoding processes.

Double Compression Detection in MPEG-4 Videos Based on Block Artifact Measurement with Variation of Prediction Footprint

In this paper, we propose a novel scheme to detect double MPEG-4 compression with block artifact analysis. An adaptive measurement of block artifact in decompressed frames is proposed and then combined with the Variation of Prediction Footprint (VPF) in an effective way. Based on such measurement, periodic analysis is used to detect double compression. The proposed scheme is verified on several publically available standard videos and compared with the state-of-the-art method. Experimental results demonstrate that it has more robust detection capability.

A space-time SURF descriptor and its application to action recognition with video words

A novel method to human action recognition is presented with the combining of a new space-time Speeded Up Robust Features (SURF) descriptor and the bag of video words (BOVW) approach. In our method, we have extended the SURF so that it can better represent the inherent spatio-temporal information of the video data for action recognition. To utilize this descriptor in the action recognition framework, the BOVW schema with a soft-weighting strategy is exploited. Experiments, conducted with the KTHs action recognition dataset, have shown that the proposed method can achieve an outstanding performance in both computing speed and accuracy contrast to the traditional methods.