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

Abstract

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.

A fast forgery frame detection method for video copy-move inter/intra-frame identification

“…Only a few state-of-the-art methods (Bestagini et al 2013;Lowe 2004;Saddique et al 2020;Su et al 2018;Subramanyam and Emmanuel 2012;Zhang et al 2015) can address video copy-move forgery detection. Subramanyam et al (Subramanyam and Emmanuel 2012), propose a Histogram of Oriented Gradients (HOG) feature matching and video compression properties to address only intra-frame forgery in MPEG4 format.…”

“…It is known that a higher F 1 score denotes performance better. Finally, the experiments are implemented on a computer with an Intel (R) Core i7-8700 @3.20 There are several state-of-the-art methods, including the Dense moment feature index and best match algorithm with radial-harmonic-Fourier moments (DMFIBM) (Zhong et al 2020), Bestagini et al (Bestagini et al 2013), MRPL method (Saddique et al 2020). However, the methods in the literatures of (Subramanyam and Emmanuel 2012) cannot be applied to real datasets like GRIP because they are with very restrictive assumptions on forgery videos.…”

“…However, this work can only detect the inter-frame forgery and lacks robustness to resist compression. The deep neural network (DNN) schemes, e.g., Motion Residual and Parasitic Layers (MRPL) (Saddique et al 2020)are proposed to address the video copy-move forgery issue. However, MPRL only detect the differences between the forgery frame start and end, and the adjacent frames.…”

Detection and localization of image tampering in digital images with fused features

“…Tampering detection has been explored in many forensic methods, and localization of tampered images has recently become one of the most important methods for researchers 12‐17 . Tampering with an image (or a video) is described as “adding or removing essential features from an image (or a video) despite leaving any apparent indications of tampering.” Copy‐move, splicing, re‐sampling, resizing, noise changes and/or blurring, retouching, JPEG compression, luminance nonlinearities, and lighting inconsistencies are some of the most popular strategies adopted by attackers 18‐22 . The three prevalent tampering operations are (i) removing (or attempting to hide) an area in an image, (ii) inserting a new entity into such an image, and (iii) misrepresentation of image data (e.g., resizing an object within the image).…”

“…Copy-move, splicing, re-sampling, resizing, noise changes and/or blurring, retouching, JPEG compression, luminance nonlinearities, and lighting inconsistencies are some of the most popular strategies adopted by attackers. [18][19][20][21][22] The three prevalent tampering operations are (i) removing (or attempting to hide) an area in an image, (ii) inserting a new entity into such an image, and (iii) misrepresentation of image data…”