A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis

Lin, Cheng-Shian; Tsay, Jyh-Jong

doi:10.1016/j.diin.2014.03.016

Cited by 65 publications

(45 citation statements)

References 19 publications

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“…The performance of the proposed technique is compared with techniques proposed in [27], [30] and [43]. These selected techniques are considered because of their popularity and average performance rate of 96.61, 93.40 and 97.52 respectively for video inpainting forgery detection over the years.…”

Section: Comparison With Other Detection Techniquesmentioning

confidence: 99%

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Digital Video Inpainting Detection Using Correlation Of Hessian Matrix

Bagiwa

Wahab

Idris

et al. 2016

MJCS

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The use of digital video during forensic investigation helps in providing evidence related to crime scene. However, due to freely available user friendly video editing tools, the forgery of acquired digital videos that are used as evidence in a law suit is now simpler and faster. As a result, it has become easier for manipulators to alter the contents of digital evidence. For instance, inpainting technique is used to remove an object from a video without leaving any artefact of illegal tampering. Therefore, this paper presents a technique for detecting and locating inpainting forgery in a video sequence with static camera motion. Our technique exploits statistical correlation of Hessian matrix (SCHM) to detect and locate tampered regions within a video sequence. The results of our experiments prove that the technique effectively detect and locate areas which are tampered using both texture and structure based inpainting with an average precision rate of 99.79% and an average false positive rate of 0.29%.

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Section: Comparison With Other Detection Techniquesmentioning

confidence: 99%

“…An improvement on the work of [42] was presented in [43] by automatically locating the exact region of tampering in a 3D video. The technique detects and locate tampered region in a video from inpainting using spatio-temporal slicing and coherence analysis (STCA).…”

Section: Video Inpaintingmentioning

confidence: 99%

Digital Video Inpainting Detection Using Correlation Of Hessian Matrix

Bagiwa

Wahab

Idris

et al. 2016

MJCS

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“…In [87], the authors proposed an approach for detecting and localizing region-level forgeries in videos. The approach was designed to detect irregularities caused by inpainting in spatio-temporal coherence between consecutive frames.…”

Section: Pixel-similarity and Correlation Analysis-based Techniquesmentioning

confidence: 99%

“…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.…”

Section: Comparative Analysis Of Copy-paste Forgery Detection Techniquesmentioning

confidence: 99%

Video content authentication techniques: a comprehensive survey

Singh

Aggarwal

2017

Multimedia Systems

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“…Such approaches are utilized to detect any unauthorized manipulation performed either at intra-frame level or at inter-frame level. Forgery performed at intra-frame level manipulates a frame at pixel level, object level [6], [7] or at entire frame level [8]. On the other hand, Inter-frame tampering can be performed by mere removal [9], insertion [10] and/or replication [11] of a frame or set of frames to/from a video.…”

Section: Introductionmentioning

confidence: 99%

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

Kingra

Aggarwal

Singh

2017

IJECE

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We are living in an age where use of multimedia technologies like digital recorders and mobile phones is increasing rapidly. On the other hand, digital content manipulating softwares are also increasing making it easy for an individual to doctor the recorded content with trivial consumption of time and wealth. Digital multimedia forensics is gaining utmost importance to restrict unethical use of such easily available tampering techniques. These days, it is common for people to record videos using their smart phones. We have also witnessed a sudden growth in the use of surveillance cameras, which we see inhabiting almost every public location. Videos recorded using these devices usually contains crucial evidence of some event occurence and thereby most susceptible to inter-frame forgery which can be easily performed by insertion/removal/replication of frame(s). The proposed forensic technique enabled detection of inter-frame forgery in H.264 and MPEG-2 encoded videos especially mobile recorded and surveillance videos. This novel method introduced objectivity for automatic detection and localization of tampering by utilizing prediction residual gradient and optical flow gradient. Experimental results showed that this technique can detect tampering, regardless of the video codec and recording device utilized and number of frames tampered.

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A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis

Cited by 65 publications

References 19 publications

Digital Video Inpainting Detection Using Correlation Of Hessian Matrix

Digital Video Inpainting Detection Using Correlation Of Hessian Matrix

Video content authentication techniques: a comprehensive survey

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

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