Coarse-to-Fine Copy-Move Forgery Detection for Video Forensics

Jia, Shan; Wang, Chao; Wang, Hao; Feng, Chunhui; Wang, Tao

doi:10.1109/access.2018.2819624

Cited by 63 publications

(51 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the process of increasing or decreasing the frame rates of forged videos, the frames are either deleted or duplicated or interpolated. The frame duplication will increase the correlation between the frames [3], while deletion of frames will decrease it. Therefore, altering frame rate will disturb the various vital parameters of a video [3], thus making the forgery detection more difficult.…”

Section: Results and Disscussionmentioning

confidence: 99%

“…In the literature, an extensive work is done in developing frame level forgery detection techniques. In [3], Jia et al . developed a method to detect frame copy‐move forgery based on optical flow (OF) and stable parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Further, to remove anomalies from detection results, the authors developed algorithms based on correlation parameters of OF. This method [3] shows poor performance for large static scene videos and used low resolution video dataset SULFA. Stamm et al [4] proposed a method to inspect the peaks generated due to frame deletion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CNN based localisation of forged region in object‐based forgery for HD videos

Kohli

Gupta

Singhal

2020

IET Image Processing

View full text Add to dashboard Cite

Section: Results and Disscussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CNN based localisation of forged region in object‐based forgery for HD videos

Kohli

Gupta

Singhal

2020

IET Image Processing

View full text Add to dashboard Cite

“…Videos tampering attacks can be categorized as: (i) spatial domain, (ii) temporal domain, and (iii) spatio-temporal domain [3]. In spatial domain, different objects can be added, removed or replaced within a video frame or a series of frames, whereas in the temporal domain, a number of frames are added, removed or replaced from the video [4]. Spatiotemporal domain is the combination of spatial and temporal domain.…”

Section: Introductionmentioning

confidence: 99%

Classification of Authentic and Tampered Video Using Motion Residual and Parasitic Layers

et al. 2020

View full text Add to dashboard Cite

These days, videos can be easily recorded, altered and shared on social and electronic media for deception and false propaganda. However, due to sophisticated nature of the content alteration tools, alterations remain inconspicuous to the naked eye and it is a challenging task to differentiate between authentic and tampered videos. During the process of video tampering the traces of objects, which are removed or modified, remain in the frames of a video. Based on this observation, in this study, a new method is introduced for discriminating authentic and tampered video clips. This method is based on deep model, which consists of three types of layers: motion residual (MR), convolutional neural network (CNN), and parasitic layers. The MR layer highlights the tampering traces by aggregation of frames. The CNN layers encode these tampering traces and are learned using transfer learning. Finally, parasitic layers classify the video clip (VC) as authentic or tampered. The parasitic layers are learned using an efficient learning method based on extreme learning theory; they enhance the performance in terms of efficiency and accuracy. Intensive experiments were performed on various benchmark datasets to validate the performance and the robustness of the method; it achieved 98.89% accuracy. Comparative analysis shows that the proposed method outperforms the state-of-the-art methods.

show abstract

“…In [8], the authors used the correlation coefficient of sequences of frames DCT means. In [9,10], used optical flow to detect frame insertion and frame deletion. In [11], the authors used Zernike opponent chromaticity moments to detect frame insertion, frame deletion, and duplication of frame sequences.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional Region Forgery Detection and Localization in Videos

Nguyen¹,

Hu²,

Amin³

et al. 2019

IJIGSP

View full text Add to dashboard Cite

Nowadays, with the extensive use of cameras in many areas of life, every day millions of videos are uploaded on the internet. In addition, with rapidly developing video editing software applications, it has become easier to forge any video. These software applications have made it challenging to detect forged videos, especially with forged videos have duplication of three-dimensional (3-D) regions. Recently, there has been increased interest in detecting forged videos, but there are very limited studies to detect forged videos which were duplicated 3-D regions. So, our research focused on this weakness and proposed a new method, which can be used for detecting and locating 3-D duplicated regions in videos based on the phase-correlation of 3-D regions residual more efficiently. To evaluate the efficiency of the proposed method, we experimented with two realistic datasets VFDD-3D and REWIND-3D. The results of the experiments proved that the proposed method is efficient and robust for detecting small 3-D regions duplication and frame sequences duplication, especially localization of duplication forgery in videos has shown impressive results.

show abstract

Coarse-to-Fine Copy-Move Forgery Detection for Video Forensics

Cited by 63 publications

References 27 publications

CNN based localisation of forged region in object‐based forgery for HD videos

CNN based localisation of forged region in object‐based forgery for HD videos

Classification of Authentic and Tampered Video Using Motion Residual and Parasitic Layers

Three-dimensional Region Forgery Detection and Localization in Videos

Contact Info

Product

Resources

About