6 research outputs found
Autoencoder with recurrent neural networks for video forgery detection
Video forgery detection is becoming an important issue in recent years,
because modern editing software provide powerful and easy-to-use tools to
manipulate videos. In this paper we propose to perform detection by means of
deep learning, with an architecture based on autoencoders and recurrent neural
networks. A training phase on a few pristine frames allows the autoencoder to
learn an intrinsic model of the source. Then, forged material is singled out as
anomalous, as it does not fit the learned model, and is encoded with a large
reconstruction error. Recursive networks, implemented with the long short-term
memory model, are used to exploit temporal dependencies. Preliminary results on
forged videos show the potential of this approach.Comment: Presented at IS&T Electronic Imaging: Media Watermarking, Security,
and Forensics, January 201
Video Forgery Detection: A Comprehensive Study of Inter and Intra Frame Forgery With Comparison of State-Of-Art
Availability of sophisticated and low-cost smart phones, digital cameras, camcorders, surveillance CCTV cameras are extensively used to create videos in our daily life. The prevalence of video sharing techniques presently available in the market are: YouTube, Facebook, Instagram, snapchat and many more are in utilization to share the information related to videos. Besides this, there are many software which can edit the content of video: Window Movie Maker, Video Editor, Adobe Photoshop etc., with this available software anyone can edit the video content which is called as “Forgery” if edited content is harmful. Usually, videos play a vital role in terms of proof in crime scene. The Victim is judged by the proof submitted by the lawyer to the court. Many such cases have evidenced that the video being submitted as proof is been forged. Checking the authentication of the video is most important before submitting as proof. There has been a rapid development in deep learning techniques which have created deepfake videos where faces are replaced with other faces which strongly made a belief of saying “Seeing is no longer believing”. The available software which can morph the faces are FakeApp, FaceSwap etc., the increased technology really made the Authentication of proofs very doubtful and un-trusty which are not accepted as proof without proper validation of the video. The survey gives the methods that are capable of accurately computing the videos and analyses to detect different kinds of forgeries. It has revealed that most of the existing methods are relying on number of tampered frames. The proposed techniques are with compression, double compression codec videos where research is being carried out from 2016 to present. This paper gives the comprehensive study of techniques, algorithms and applications designed and developed to detect forgery in videos
A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization
We propose a new algorithm for the reliable detection and localization of
video copy-move forgeries. Discovering well crafted video copy-moves may be
very difficult, especially when some uniform background is copied to occlude
foreground objects. To reliably detect both additive and occlusive copy-moves
we use a dense-field approach, with invariant features that guarantee
robustness to several post-processing operations. To limit complexity, a
suitable video-oriented version of PatchMatch is used, with a multiresolution
search strategy, and a focus on volumes of interest. Performance assessment
relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide
variety of challenging situations. Experimental results show the proposed
method to detect and localize video copy-moves with good accuracy even in
adverse conditions
Video copy-move forgery detection scheme based on displacement paths
Sophisticated digital video editing tools has made it easier to tamper real videos and create perceptually indistinguishable fake ones. Even worse, some post-processing effects, which include object insertion and deletion in order to mimic or hide a specific event in the video frames, are also prevalent. Many attempts have been made to detect such as video copy-move forgery to date; however, the accuracy rates are still inadequate and rooms for improvement are wide-open and its effectiveness is confined to the detection of frame tampering and not localization of the tampered regions. Thus, a new detection scheme was developed to detect forgery and improve accuracy. The scheme involves seven main steps. First, it converts the red, green and blue (RGB) video into greyscale frames and treats them as images. Second, it partitions each frame into non-overlapping blocks of sized 8x8 pixels each. Third, for each two successive frames (S2F), it tracks every block’s duplicate using the proposed two-tier detection technique involving Diamond search and Slantlet transform to locate the duplicated blocks. Fourth, for each pair of the duplicated blocks of the S2F, it calculates a displacement using optical flow concept. Fifth, based on the displacement values and empirically calculated threshold, the scheme detects existence of any deleted objects found in the frames. Once completed, it then extracts the moving object using the same threshold-based approach. Sixth, a frame-by-frame displacement tracking is performed to trace the object movement and find a displacement path of the moving object. The process is repeated for another group of frames to find the next displacement path of the second moving object until all the frames are exhausted. Finally, the displacement paths are compared between each other using Dynamic Time Warping (DTW) matching algorithm to detect the cloning object. If any pair of the displacement paths are perfectly matched then a clone is found. To validate the process, a series of experiments based on datasets from Surrey University Library for Forensic Analysis (SULFA) and Video Tampering Dataset (VTD) were performed to gauge the performance of the proposed scheme. The experimental results of the detection scheme were very encouraging with an accuracy rate of 96.86%, which markedly outperformed the state-of-the-art methods by as much as 3.14%