Blind Video Tamper Detection Based on Fusion of Source Features

Digital video tampering detection: An overview of passive techniques

“…Bit rate reduction reduces the performance of the system. Ghost shadow artifact (Zhang et al, 2009) Cannot accurately locate the tampered areas in each frame; works well in static background videos only Noise and quantization residue (Chetty et al, 2010;Goodwin and Chetty, 2011) Sensitive to noise, too high or too low illumination; works well static background videos only Histogram of Oriented Gradients (HOG) (Subramanyam and Emmanuel, 2012) Works with fixed GOP; copypaste tampering alone is addressed VPF, histogram of DCT coefficients (Labartino et al, 2013) Presence of B-frames are not considered; works with Variable Bit Rate (VBR) coding only Spatio-temporal coherence (Lin and Tsay, 2014) Performance decreases with increase in compression Difference between current & nontampered reference frame (Su et al, 2015a) Works with static background videos; detection accuracy decreases when the deleted foreground is very small, or too fast moving Zernike moments and 3D patch match (D'Amiano et al, 2015) Accuracy is very low M A N U S C R I P T…”

“…Pixels in the spliced regions are differentiated using MAP estimation of the noise model where the NLFs of these regions are inconsistent with the rest of the video. In (Chetty et al, 2010) and (Goodwin and Chetty, 2011), video tamper detection in low-bandwidth Internet streamed videos using the residue (the noise and the quantization residue) features from intra-frame and inter frame pixel sub-blocks, their transformation in the cross-modal subspace using Latent Semantic Analysis (LSA), Cross-modal Factor Analysis (CFA), and Canonical Correlation Analysis (CCA) and their subsequent multimodal fusion is discussed.…”

Video content authentication techniques: a comprehensive survey

“…Another technique for copy-move detection was proposed in [83,84] which was based on the hypothesis that correlation attributes of pixel sub-blocks within as well as between the frames were bound to be disarranged by tampering attacks, such as double compression, retouching, or resampling. The authors extracted noise residue and quantization residue features from adjacent frames and then performed correlation analysis using Canonical Correlation Analysis (CCA), Cross-modal Factor Analysis (CFA), and Latent Semantic Analysis (LSA).…”

“…We analyzed the performances of the following copy-paste detection techniques: the noise-based approaches proposed in [38,82], the noise and quantization residue-based scheme of [84], motion-residue-based approach proposed in [86], the pixel-coherence analysis technique 2 suggested in [87], the object-based technique suggested in [94], and the optical-flow-based method proposed in [98]. Table 2 presents a comparative summary of the outcomes, as a function of various compression quality factors (QF) and bitrates.…”

Video Authentication against Set of Temporal Tampering

“…Unlike FR or RR (often called as active tampering detection techniques), while tampering detection under NR mode (often called as blind or passive tampering detection), forensic experts do not have any information about actual contents of video sequence to be examined [10,11]. Compared to FR or RR, blind tampering detection (NR Copyright ⓒ 2017 SERSC mode) is relatively new research focus and puts lot of challenges against forensic experts to detect tampering in manipulated video sequences.…”