Interframe Video Research Articles

Frame Duplication Forgery Detection in Surveillance Video Sequences Using Textural Features

Frame duplication forgery is the most common inter-frame video forgery type to alter the contents of digital video sequences. It can be used for removing or duplicating some events within the same video sequences. Most of the existing frame duplication forgery detection methods fail to detect highly similar frames in the surveillance videos. In this paper, we propose a frame duplication forgery detection method based on textural feature analysis of video frames for digital video sequences. Firstly, we compute the single-level 2-D wavelet decomposition for each frame in the forged video sequences. Secondly, textural features of each frame are extracted using the Gray Level of the Co-Occurrence Matrix (GLCM). Four second-order statistical descriptors, Contrast, Correlation, Energy, and Homogeneity, are computed for the extracted textural features of GLCM. Furthermore, we calculate four statistical features from each frame (standard deviation, entropy, Root-Mean-Square RMS, and variance). Finally, the combination of GLCM’s parameters and the other statistical features are then used to detect and localize the duplicated frames in the video sequences using the correlation between features. Experimental results demonstrate that the proposed approach outperforms other state-of-the-art (SOTA) methods in terms of Precision, Recall, and F1Score rates. Furthermore, the use of statistical features combined with GLCM features improves the performance of frame duplication forgery detection.

Inter-Frame Video Compression based on Adaptive Fuzzy Inference System Compression of Multiple Frame Characteristics

Video compression is used for storage or bandwidth efficiency in clip video information. Video compression involves encoders and decoders. Video compression uses intra-frame, inter-frame, and block-based methods. Video compression compresses nearby frame pairs into one compressed frame using inter-frame compression. This study defines odd and even neighboring frame pairings. Motion estimation, compensation, and frame difference underpin video compression methods. In this study, adaptive FIS (Fuzzy Inference System) compresses and decompresses each odd-even frame pair. First, adaptive FIS trained on all feature pairings of each odd-even frame pair. Video compression-decompression uses the taught adaptive FIS as a codec. The features utilized are "mean", "std (standard deviation)", "mad (mean absolute deviation)", and "mean (std)". This study uses all video frames' average DCT (Discrete Cosine Transform) components as a quality parameter. The adaptive FIS training feature and amount of odd-even frame pairings affect compression ratio variation. The proposed approach achieves CR=25.39% and P=80.13%. "Mean" performs best overall (P=87.15%). "Mean (mad)" has the best compression ratio (CR=24.68%) for storage efficiency. The "std" feature compresses the video without decompression since it has the lowest quality change (Q_dct=10.39%).

An Algorithm for Learning Orthonormal Matrix Codebooks for Adaptive Transform Coding.

This paper proposes a novel data-driven approach to designing orthonormal transform matrix codebooks for adaptive transform coding of any non-stationary vector processes which can be considered locally stationary. Our algorithm, which belongs to the class of block-coordinate descent algorithms, relies on simple probability models such as Gaussian or Laplacian for transform coefficients to directly minimize with respect to the orthonormal transform matrix the mean square error (MSE) of scalar quantization and entropy coding of transform coefficients. A difficulty commonly encountered in such minimization problems is imposing the orthonormality constraint on the matrix solution. We get around this difficulty by mapping the constrained problem in Euclidean space to an unconstrained problem on the Stiefel manifold and leveraging known algorithms for unconstrained optimization on manifolds. While the basic design algorithm directly applies to non-separable transforms, an extension to separable transforms is also proposed. We present experimental results for adaptive transform coding of still images and video inter-frame prediction residuals, comparing the transforms designed using the proposed method and a number of other content-adaptive transforms recently reported in the literature.

Enhancement Digital Forensic Approach for Inter-Frame Video Forgery Detection Using a Deep Learning Technique

The digital world has been witnessing a fast progress in technology, which led to an enormous increase in using digital devices, such as cell phones, laptops, and digital cameras. Thus, photographs and videos function as the primary sources of legal proof in courtrooms concerning any incident or crime. It has become important to prove the trustworthiness of digital multimedia. Inter-frame video forgery one of common types of video manipulation performed in temporal domain. It deals with inter-frame video forgery detection that involves frame deletion, insertion, duplication, and shuffling. Deep Learning (DL) techniques have been proven effective in analysis and processing of visual media. Dealing with video data needs to handle the third dimension (the time dimension), which means extracting temporal features as well as spatial features. The proposed model is built based on the Three Dimension Convolution Neural Network (3D-CNN). Through pre-processing operation that introduced difference frames that pick up the difference in successive adjacent frames, which provide a large quantity of temporal information and lead to enhance the effectiveness of the proposed model. The model achieves high accuracy of 99%.

Inter-frame video forgery detection using UFS-MSRC algorithm and LSTM network

The forgery involved in region duplication is a common type of video tampering, where the traditional techniques used to detect video tampering are ineffective and inefficient for the forged videos under complex backgrounds. To overcome this issue, a novel video forgery detection model is introduced in this research paper. Initially, the input video sequences are collected from Surrey University Library for Forensic Analysis (SULFA) and Sondos datasets. Further, spatiotemporal averaging method is carried out on the collected video sequences to obtain background information with a pale of moving objects for an effective video forgery detection. Next, feature extraction is performed using the GoogLeNet model for extracting the feature vectors. Then, the Unsupervised Feature Selection with Multi-Subspace Randomization and Collaboration (UFS-MSRC) approach is used to choose the discriminative feature vectors that superiorly reduce the training time and improve the detection accuracy. Finally, long short-term memory (LSTM) network is applied for forgery detection in the different video sequences. The experimental evaluation illustrated that the UFS-MSRC with LSTM model attained 98.13% and 97.38% of accuracy on SULFA and Sondos datasets, where the obtained results are better when compared to the existing models in video forgery detection.

Sequence-Level Reference Frames in Video Coding

The proliferation of low-cost DRAM chipsets now begins to allow for the consideration of substantially-increased decoded picture buffers in advanced video coding standards such as HEVC, VVC, and Google VP9. At the same time, the increasing demand for rapid scene changes and multiple scene repetitions in entertainment or broadcast content indicates that extending the frame referencing interval to tens of minutes or even the entire video sequence may offer coding gains, as long as one is able to identify frame similarity in a computationally- and memory-efficient manner. Motivated by these observations, we propose a “stitching” method that defines a reference buffer and a reference frame selection algorithm. Our proposal extends the referencing interval of inter-frame video coding to the entire length of video sequences. Our reference frame selection algorithm uses well-established feature descriptor methods that describe frame structural elements in a compact and semantically-rich manner. We propose to combine such compact descriptors with a similarity scoring mechanism in order to select the frames to be “stitched” to reference picture buffers of advanced inter-frame encoders like HEVC, VVC, and VP9 without breaking standard compliance. Our evaluation on synthetic and real-world video sequences with the HEVC and VVC reference encoders shows that our method offers significant rate gains, with complexity and memory requirements that remain manageable for practical encoders and decoders.

Multiple Forgery Detection in Video Using Convolution Neural Network

With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software, the authenticity of records is at high risk, especially in video. There is a dire need to detect such problem and do the necessary actions. In this work, we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical computations. The proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to these features to find the forgery in the video. This work calculates the correlation coefficient from the deep features of the adjacent frames rather than calculating directly from the frames. We divide the procedure of forgery detection into two phases–video forgery detection and video forgery classification. In video forgery detection, this approach detect input video is original or tampered. If the video is not original, then the video is checked in the next phase, which is video forgery classification. In the video forgery classification, method review the forged video for insertion forgery, deletion forgery, and also again check for originality. The proposed work is generalized and it is tested on two different datasets. The experimental results of our proposed model show that our approach can detect the forgery with the accuracy of 91% on VIFFD dataset, 90% in TDTV dataset and classify the type of forgery–insertion and deletion with the accuracy of 82% on VIFFD dataset, 86% on TDTV dataset. This work can helps in the analysis of original and tempered video in various domain.

Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection.

Digital video forensics plays a vital role in judicial forensics, media reports, e-commerce, finance, and public security. Although many methods have been developed, there is currently no efficient solution to real-life videos with illumination noises and jitter noises. To solve this issue, we propose a detection method that adapts to brightness and jitter for video inter-frame forgery. For videos with severe brightness changes, we relax the brightness constancy constraint and adopt intensity normalization to propose a new optical flow algorithm. For videos with large jitter noises, we introduce motion entropy to detect the jitter and extract the stable feature of texture changes fraction for double-checking. Experimental results show that, compared with previous algorithms, the proposed method is more accurate and robust for videos with significant brightness variance or videos with heavy jitter on public benchmark datasets.

Authentication of Video Evidence for Forensic Investigation: A Case of Nigeria

Video shreds of evidence are usually admissible in the court of law all over the world. However, individuals manipulate these videos to either defame or incriminate innocent people. Others indulge in video tampering to falsely escape the wrath of the law against misconducts. One way impostors can forge these videos is through inter-frame video forgery. Thus, the integrity of such videos is under threat. This is because these digital forgeries seriously debase the credibility of video contents as being definite records of events. This leads to an increasing concern about the trustworthiness of video contents. Hence, it continues to affect the social and legal system, forensic investigations, intelligence services, and security and surveillance systems as the case may be. The problem of inter-frame video forgery is increasingly spontaneous as more video-editing software continues to emerge. These video editing tools can easily manipulate videos without leaving obvious traces and these tampered videos become viral. Alarmingly, even the beginner users of these editing tools can alter the contents of digital videos in a manner that renders them practically indistinguishable from the original content by mere observations. This paper, however, leveraged on the concept of correlation coefficients to produce a more elaborate and reliable inter-frame video detection to aid forensic investigations, especially in Nigeria. The model employed the use of the idea of a threshold to efficiently distinguish forged videos from authentic videos. A benchmark and locally manipulated video datasets were used to evaluate the proposed model. Experimentally, our approach performed better than the existing methods. The overall accuracy for all the evaluation metrics such as accuracy, recall, precision and F1-score was 100%. The proposed method implemented in the MATLAB programming language has proven to effectively detect inter-frame forgeries.

FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame Interpolation

The problem of video inter-frame interpolation is an essential task in the field of image processing. Correctlyincreasing the number of frames in the recording while maintaining smooth movement allows to improve thequality of played video sequence, enables more effective compression and creating a slow-motion recording. Thispaper proposes the FastRIFE algorithm, which is some speed improvement of the RIFE (Real-Time IntermediateFlow Estimation) model. The novel method was examined and compared with other recently published algorithms.All source codes are available at:https://gitlab.com/malwinq/interpolation-of-images-for-slow-motion-videos.

Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Video forensics is one of the hot topics in multimedia forensics. Nowadays, spreading fake videos across the internet is becoming a profession mainly in politics and entertainment. In this study, a novel two-stage inter-frame video forgery detection technique is proposed. The first stage analyses spatio-temporal feature flow consistency to detect suspicious tamper points. Identifying the type of forgery and validating the recovered video are done in the second stage. Earth mover's distance is used as a similarity metric in both stages. The authors concentrate on a robust inter-frame forgery detection approach which can be applied for any challenging video. Compression at a higher rate, noise addition, and filtering are the anti-forensic tricks used by forgers to fool forensic techniques. However, most of the literature in video forgery detection has handled these issues as post-processing attacks and reported lesser accuracies for it. Hence the authors propose a robust and efficient forgery detection technique capable of identifying all kinds of inter-frame forgeries in videos. Experimental evaluation of the public video data set shows that the proposed approach outperforms existing approaches with an improved rate of robustness.

1D representation of Laplacian eigenmaps and dual k ‐nearest neighbours for unified video coding

This study proposes a framework of video coding based on Laplacian eigenmaps (LEM) and its related embedding and reconstruction algorithm (ERA). Firstly, a one-dimensional (1D) representation of LEM is adopted to achieve an extremely low bit per pixel (BPP). Secondly, dual k-nearest neighbours, which keeps neighbour relationships both in high-dimensional data space and low-dimensional representation space and overcomes the disadvantage of classical non-linear dimensionality reduction methods which cannot preserve the neighbour properties in both of the spaces, based ERA of LEM is employed to gain extraordinarily high peak-signal-to-noise ratio (PSNR). Thirdly, a unified framework of video coding is fit for intra-frame, inter-frame and multi-view video coding. Finally, it is evaluated by simulation experiments that, in the situation of low bitrate transmission, the proposed method can attain better performance of BPP and PSNR than that of the state-of-the-art methods, such as highly efficient video coding.

Video inter-frame tampering detection algorithm fusing multiple features

Video inter-frame tampering detection algorithm fusing multiple features

Inter-frame video image generation based on spatial continuity generative adversarial networks

This paper proposes a method for generating inter-frame video images based on spatial continuity generative adversarial networks (SC-GANs) to smooth the playing of low-frame rate videos and to clarify blurry image edges caused by the use of traditional methods to improve the video frame rate. Firstly, the auto-encoder is used as a discriminator and Wasserstein distance is applied to represent the difference between the loss distribution of the real sample and the generated sample, instead of the typical method of generative adversarial networks to directly match data distribution. Secondly, the hyperparameter between generator and discriminator is used to stabilize the training process, which effectively prevents the model from collapsing. Finally, taking advantage of the spatial continuity of the image features of continuous video frames, an optimal value between two consecutive frames is found by Adam and then mapped to the image space to generate inter-frame images. In order to illustrate the authenticity of the generated inter-frame images, PSNR and SSIM are adopted to evaluate the inter-frame images, and the results show that the generated inter-frame images have a high degree of authenticity. The feasibility and validity of the proposed method based on SC-GAN are also verified.

An Overview of Digital Video Tampering Detection Using Passive Methods and D-Hash Algorithm

Video tampering and integrity detection can be defined as methods of alteration of the contents of the video which will enable it to hide objects, an occasion or adjust the importance passed on by the collection of images in the video. Modification of video contents is growing rapidly due to the expansion of the video procurement gadgets and great video altering programming devices. Subsequently verification of video files is transforming into something very vital. Video integrity verification aims to search out the hints of altering and subsequently asses the realness and uprightness of the video. These strategies might be ordered into active and passive techniques. Therefore our area of concern in this paper is to present our views on different passive video tampering detection strategies and integrity check. Passive video tampering identification strategies are grouped into consequent three classifications depending on the type of counterfeiting as: Detection of double or multiple compressed videos, Region altering recognition and Video inter-frame forgery detection. So as to detect the tampering of the video, it is split into frames and hash is generated for a group of frames referred to as Group of Pictures. This hash value is verified by the receiver to detect tampering.

Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between Haralick coded frames

With the immensely growing rate of cyber forgery today, the integrity and authenticity of digital multimedia data are highly at stake. In this work, we deal with forensic investigation of cyber forgery in digital videos. The most common types of inter-frame forgery in digital videos are frame insertion, deletion and duplication attacks. A number of significant researches have been carried out in this direction, in the past few years. In this paper, we propose a two-step forensic technique to detect frame insertion, deletion and duplication types of video forgery. In the first step, we detect outlier frames, based on Haralick coded frame correlation; and in the second step, we perform a finer degree of detection, to eliminate false positives, hence to optimize the forgery detection accuracy. Our experimental results prove that the proposed method outperforms the state–of–the–art with an average F1 score of 0.97 in terms of inter–frame video forgery detection accuracy.

A multi-channel approach through fusion of audio for detecting video inter-frame forgery

The forgery operation of digital video in the temporal domain is often accompanied by the synchronization of the audio channel operation. In this paper, we proposed a fusion of audio forensics detection methods for video inter-frame forgery. First, the audio channel of the video is extracted, and discrete wavelet packet decomposition and analysis of singularity points of audio signals are used to locate the forged singularity points. Next, features of each frame of the video are extracted with the perceptual hash and used to calculate the similarity between consecutive frames, to locate the forgery position in the video frame sequence. We fused the results of the audio channel and the video frame sequence channel. The QDCT feature is used to further fine detect the suspected forgery location. Our method can position replication source locations for copy-move forgery. Experiments show that our method has higher accuracy and better performance in comparison with similar methods, especially on the delete forgery operation.

Video Inter-frame Forgery Detection: A Survey

Objectives: This paper summarizes all the proposed techniques involved in digital video inter-frame forgery detection for MPEG-1, 2, 4 and H.264/AVC encoded videos. Methods/Statistical Analysis: Double compression detection techniques are classified here on the basis of footprints analyzed during detection. The detection methods designed for videos that use fixed GOP structure for first and any of the subsequent compression are different from the videos that use different GOP structure. Video inter-frame forgery detection techniques are then analyzed on the basis of type of forgery they detect and the type of codec used for video encoding. Findings: Digital videos often provide forensic evidence in legal, medical and surveillance applications but are more prone to inter-frame forgeries, which are not only easy to perform but are equally difficult to detect as well. The analysis of the literature ascertained that majority of the proposed techniques are dependent on the number of frames tampered and video codec used to encode the videos. Among these proposed techniques, double MPEG compression was best detected using the technique which utilizes Benford’s law and was proposed by Chen and Shi on MPEG-1 and MPEG-2 encoded videos. On the other hand, Wang et al. gave sound results for all kinds of inter-frame forgeries on MPEG-2 encoded videos by utilizing the measure of optical flow consistency. Since very few authors focussed on forgery detection in MPEG-4 encoded videos and thereby such techniques have not been discussed in many survey papers. Moreover, digital cameras especially surveillance cameras which generate massive amount of videos these days have built-in MPEG-4 codec because it offers a better compression rate. Application/Improvements: Video forensics domain, therefore, is in dire need of a technique that will detect any kind of inter-frame forgery in MPEG-4 encoded videos.

Malicious inter-frame video tampering detection in MPEG videos using time and spatial domain analysis of quantization effects

In this paper, a new algorithm is proposed for forgery detection in MPEG videos using spatial and time domain analysis of quantization effect on DCT coefficients of I and residual errors of P frames. The proposed algorithm consists of three modules, including double compression detection, malicious tampering detection and decision fusion. Double compression detection module employs spatial domain analysis using first significant digit distribution of DCT coefficients in I frames to detect single and double compressed videos using an SVM classifier. Double compression does not necessarily imply the existence of malignant tampering in the video. Therefore, malicious tampering detection module utilizes time domain analysis of quantization effect on residual errors of P frames to identify malicious inter-frame forgery comprising frame insertion or deletion. Finally, decision fusion module is used to classify input videos into three categories, including single compressed videos, double compressed videos without malicious tampering and double compressed videos with malicious tampering. The experimental results and the comparison of the results of the proposed method with those of other methods show the efficiency of the proposed algorithm.

Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding

In this paper, a new passive approach is proposed for tampering detection and localization in MPEGx coded videos. The proposed algorithm can detect frame insertion or deletion and double compression with different GOP structures and lengths. To devise the proposed algorithm, the traces of quantization error on residual errors of P frames are mathematically studied. Then, based on the obtained guidelines, a new algorithm is proposed to detect quantization-error-rich areas in the P frames and reduce the effect of motion on residual errors of P frames. Subsequently, a wavelet-based algorithm is proposed to enrich the traces of quantization error in the frequency domain. Finally, the processed and spatially constrained residual errors of P frames are employed to detect and localize video forgery in the temporal domain. Experimental results and a comparison of the proposed method with an existing approach show the efficiency of the proposed algorithm especially for videos with high compression rates.