Video interframe forgery detection: Classification, technique & new dataset

Multimedia communication as well as other related innovations are gaining tremendous growth in the modern technological era. Even though digital content has traditionally proved to be a piece of legitimate evidence. But the latest technologies have lessened this trust, as a variety of video editing tools have been developed to modify the original video. Therefore, in order to resolve this problem, a new technique has been proposed for the detection of duplicate video sequences. The present paper utilizes gray values to extract Hu moment features in the current frame. These features are further used for classification of video as authentic or forged. Afterwards there was also need to validate the proposed technique using training and test dataset. But the scarcity of training and test datasets, however, is indeed one of the key problems to validate the effectiveness of video tampering detection techniques. In this perspective, the Video Forensics Library for Frame Duplication (VLFD) dataset has been introduced for frame duplication detection purposes. The proposed dataset is made of 210 native videos, in Ultra-HD and Full-HD resolution, captured with different cameras. Every video is 6 to 15 seconds in length and runs at 30 frames per second. All the recordings have been acquired in three different scenarios (indoor, outdoor, nature) and in landscape mode(s). VLFD includes both authentic and manipulated video files. This dataset has been created as an initial repository for manipulated video and enhanced with new features and new techniques in future.

Robust Frame Duplication Detection for Degraded Videos

To detect frame duplication in degraded videos, we proposed a coarse-to-fine approach based on locality-sensitive hashing and image registration. The proposed method consists of a coarse matching stage and a duplication verification step. In the coarse matching stage, visually similar frame sequences are preclustered by locality-sensitive hashing and considered as potential duplication candidates. These candidates are further checked by a duplication verification step. Being different from the existing methods, our duplication verification does not rely on a fixed distance (or correlation) threshold to judge whether two frames are identical. We resorted to image registration, which is intrinsically a global optimal matching process, to determine whether two frames coincide with each other. We integrated the stability information into the registration objective function to make the registration process more robust for degraded videos. To test the performance of the proposed method, we created a dataset, which consists of 3 subsets of different kinds of degradation and 117 forged videos in total. The experimental results show that our method outperforms state-of-the-art methods for most cases in our dataset and exhibits outstanding robustness under different conditions. Thanks to the coarse-to-fine strategy, the running time of the proposed method is also quite competitive.

Frame Duplication Forgery Detection and Localization Algorithm Based on the Improved Levenshtein Distance

In this digital era of technology and software development tools, low-cost digital cameras and powerful video editing software (such as Adobe Premiere, Microsoft Movie Maker, and Magix Vegas) have become available for any common user. Through these softwares, editing the contents of digital videos became very easy. Frame duplication is a common video forgery attack which can be done by copying and pasting a sequence of frames within the same video in order to hide or replicate some events from the video. Many algorithms have been proposed in the literature to detect such forgeries from the video sequences through analyzing the spatial and temporal correlations. However, most of them are suffering from low efficiency and accuracy rates and high computational complexity. In this paper, we are proposing an efficient and robust frame duplication detection algorithm to detect duplicated frames from the video sequence based on the improved Levenshtein distance. Extensive experiments were performed on some selected video sequences captured by stationary and moving cameras. In the experimental results, the proposed algorithm showed efficacy compared with the state-of-the-art techniques.

Novel Dominant KCNQ2 Exon 7 Partial In-Frame Duplication in a Complex Epileptic and Neurodevelopmental Delay Syndrome

Complex neurodevelopmental syndromes frequently have an unknown etiology, in which genetic factors play a pathogenic role. This study utilizes whole-exome sequencing (WES) to examine four members of a family with a son presenting, since birth, with epileptic-like crises, combined with cerebral palsy, severe neuromotor and developmental delay, dystonic tetraparexia, axonal motor affectation, and hyper-excitability of unknown origin. The WES study detected within the patient a de novo heterozygous in-frame duplication of thirty-six nucleotides within exon 7 of the human KCNQ2 gene. This insertion duplicates the first twelve amino acids of the calmodulin binding site I. Molecular dynamics simulations of this KCNQ2 peptide duplication, modelled on the 3D structure of the KCNQ2 protein, suggest that the duplication may lead to the dysregulation of calcium inhibition of this protein function.