Automatic location of frame deletion point for digital video forensics

Abstract

Detection of frame deletion is of great significance in the field of video forensics. Several approaches have been presented through analyzing the side effect caused by frame deletion. However, most of the current approaches can detect the existence of frame deletion but not the exact location of it. In this paper, we present a method which can directly locate the frame deletion point. Through the analysis of the distinguishing fluctuation feature of motion residual caused by frame deletion compared to interference frames and ordinary video content jitter in tampered video sequence, an algorithm based on the total motion residual of video frame is proposed to detect the frame deletion point. Moreover, an initiative processing procedure for frame motion residual and an adaptive threshold detector are introduced so that the robustness of the detection can be markedly improved. Experimental results show that the proposed algorithm is effective in generalized scenarios such as different encoding settings, rapid or slow motion sequences and multiple group of picture deletion. It also has a high performance that the true positive rate reaches 90% and the false alarm rate is less than 0.8%.

Digital video tampering detection: An overview of passive techniques

“…A C C E P T E D ACCEPTED MANUSCRIPT (Dong et al, 2012) Works with fixed GOP structure; fails when number of frames deleted is an integral multiple of GOP Periodic artifacts in the DCT coefficients of P and B-frames (Su et al, 2011) Fails when number of frames deleted is a integral multiple of GOP Sequence of Average Residual of P-frames (SARP) (Liu et al, 2014) Works with fixed GOP structure only and fails when number of frames deleted is an integral multiple of GOP Differences of mean motion residual of adjacent frames (Feng et al, 2014) As bitrate increases accuracy decreases; not good for surveillance videos where motion is very less Prediction residuals, percentage of I-MBs, quantization scales and reconstruction quality (Shanableh, 2013) Fails when number of frames deleted is a integral multiple of GOP…”

“…Since the probability distribution of the relative factor sequence of VFIs follows an approximate normal distribution, ESD is applied to identify the forgery types and locate the manipulated positions in forged videos. In (Feng et al, 2014), a method to detect the frame deletion point using motion residual of video frame is proposed. To reduce false positives (as rapid motion videos have large motion residual), the differences of mean motion residual of adjacent frames are taken.…”

“…The works in (Milani et al, 2012b) and (Rocha et al, 2011) provide an overview on video forensics. A few works in the literature have the capability to localize the tampered regions in the video ( (Subramanyam and Emmanuel, 2012); (Labartino et al, 2013); (Lin and Tsay, 2014); (D'Amiano et al, 2015); (Feng et al, 2014)) whereas others can only classify the video as tampered or not ( (Wang and Farid, 2006); (Chen et al, 2015); (Bidokhti and Ghaemmaghami, 2015); (Su et al, 2011); (Dong et al, 2012); (Stamm et al, 2012)). …”

Video Inter-frame Forgery Detection Approach for Surveillance and Mobile Recorded Videos

“…Frame insertion detection technique proposed in [10] utilized a feature called Block-wise Brightness Variance Descriptor (BBVD). Different frame removal detection techniques have been proposed in [9], [23], [12]. The technique proposed in [9] used multiple features which were based on prediction error energy and number of intra-coded macro-blocks, quantization scale and Peak Signal-to-Noise Ratio values.…”

“…The technique proposed in [9] used multiple features which were based on prediction error energy and number of intra-coded macro-blocks, quantization scale and Peak Signal-to-Noise Ratio values. Likewise, the authors in [23] and [12] utilized Enhanced Fluctuation Feature (EFF) and Sequence of Average Residual of P-frames (SARP) respectively for frame removal detection with sound accuracy. One of the frame removal detection technique proposed in [24] utilized the measure of brightness variance.…”