Video forgery detection using HOG features and compression properties

Subramanyam, A V; Emmanuel, Sabu

doi:10.1109/mmsp.2012.6343421

Cited by 81 publications

(51 citation statements)

References 13 publications

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“…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…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

“…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)). …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital video tampering detection: An overview of passive techniques

Sitara

Mehtre

2016

Digital Investigation

109

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Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital video tampering detection: An overview of passive techniques

Sitara

Mehtre

2016

Digital Investigation

109

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“…However, this approach did not work on the localization of tampered frames or on small forged regions. In reference [12], the authors studied a video forgery-detection algorithm based on the histogram of oriented gradients (HOG) feature-matching and video compression properties. This approach was effective and robust against various signal-processing manipulations.…”

Section: Introductionmentioning

confidence: 99%

Using similarity analysis to detect frame duplication forgery in videos

Yang

Huang

2014

Multimed Tools Appl

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Duplication of selected frames from a video to another location in the same video is one of the most common methods of video forgery. However, few algorithms have been suggested for detecting this tampering operation. This paper proposes an effective similarityanalysis-based method for frame duplication detection that is implemented in two stages. In the first stage, the features of each frame are obtained via SVD (Singular Value Decomposition). Next, the Euclidean distance is calculated between features of each frame and the reference frame. After dividing the video sequence into overlapping sub-sequences, the similarities between the sub-sequences are calculated, and then our algorithm identifies those video sequences with high similarity as candidate duplications. In the second stage, the candidate duplications are confirmed through random block matching. The experimental results show that our algorithm provides detection accuracy that is higher than the previous algorithms, and it has an outstanding performance in terms of time efficiency.

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“…A.V. et al [3] detected the spatial and temporal copy-paste tampering based on Histogram of Oriented Gradients (HOG) feature matching and video compression properties. For inter-frame forgeries, such as frame insertion and frame deletion forgeries, some effective methods were proposed [4,5].…”

Section: Introductionmentioning

confidence: 99%

Video Inter-Frame Forgery Identification Based on Consistency of Correlation Coefficients of Gray Values

Wang¹,

Li²,

Zhang³

et al. 2014

JCC

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Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a method based on the assumption that the correlation coefficients of gray values is consistent in an original video, while in forgeries the consistency will be destroyed. We first extract the consistency of correlation coefficients of gray values (CCCoGV for short) after normalization and quantization as distinguishing feature to identify inter-frame forgeries. Then we test the CCCoGV in a large database with the help of SVM (Support Vector Machine). Experimental results show that the proposed method is efficient in classifying original videos and forgeries. Furthermore, the proposed method performs also pretty well in classifying frame insertion and frame deletion forgeries.

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Video forgery detection using HOG features and compression properties

Cited by 81 publications

References 13 publications

Digital video tampering detection: An overview of passive techniques

Digital video tampering detection: An overview of passive techniques

Using similarity analysis to detect frame duplication forgery in videos

Video Inter-Frame Forgery Identification Based on Consistency of Correlation Coefficients of Gray Values

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