Empirical Evaluation of Motion Cue for Passive-Blind Video Tamper Detection Using Optical Flow Technique

Kumari, Poonam; Kaur, Mandeep

doi:10.1007/978-981-19-0332-8_8

Cited by 3 publications

(4 citation statements)

References 36 publications

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“…High-dimensional features are prone to overfitting, require more memory, and can lead to computational difficulties. Many approaches have high computational complexity due to high dimensional features [35][36][37], which is a significant task. Similarly, choosing an appropriate dimensionality reduction method is also challenging.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…Mathematics 2024, 12, x FOR PEER REVIEW 11 of 30 due to high dimensional features [35][36][37], which is a significant task. Similarly, choosing an appropriate dimensionality reduction method is also challenging.…”

Section: Classificationmentioning

confidence: 99%

“…Fourth, there is high computational complexity; many state-of-the-art approaches are computationally intensive because they rely on pixel-based [30,31], spatial and/or temporal correlation-based [32][33][34], or high dimensional feature-based methods [35][36][37], making it time-consuming to analyze high resolution or lengthy videos. Due to these challenges, there is a dire need for a video tampering detection system that meets these basic requirements: high accuracy, strong applicability, and high generalization capability, with good robustness.…”

Section: Introductionmentioning

confidence: 99%

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DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis

Akhtar,

Hussain,

Habib

2024

Mathematics

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Inter-frame tampering in surveillance videos undermines the integrity of video evidence, potentially influencing law enforcement investigations and court decisions. This type of tampering is the most common tampering method, often imperceptible to the human eye. Until now, various algorithms have been proposed to identify such tampering, based on handcrafted features. Automatic detection, localization, and determine the tampering type, while maintaining accuracy and processing speed, is still a challenge. We propose a novel method for detecting inter-frame tampering by exploiting a 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatic feature extraction, employing an autoencoder to significantly reduce the computational overhead by reducing the dimensionality of the feature’s space; analyzing long-range dependencies within video frames using long short-term memory (LSTM) and gated recurrent units (GRU), which helps to detect tampering traces; and finally, adding a fully connected layer (FC), with softmax activation for classification. The structural similarity index measure (SSIM) is utilized to localize tampering. We perform extensive experiments on datasets, comprised of challenging videos with different complexity levels. The results demonstrate that the proposed method can identify and pinpoint tampering regions with more than 90% accuracy, irrespective of video frame rates, video formats, number of tampering frames, and the compression quality factor.

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Section: Dimensionality Reductionmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis

Akhtar,

Hussain,

Habib

2024

Mathematics

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“… NA Optical flow is the most effective in copy-move videos that increase their accuracy until now. [ 97 ] 2022 The proposed approach used the Farneback method. Up to 98% The proposed method gives better accuracy, but computational complexity is one of the big challenges of this scheme [ 233 ] 2022 A new proposed scheme for copy-move detection at pixel-level by using fast keypoint-label matching NA However, the proposed method is still a kind of craft method that depends on many hyper parameters that affect final performance.…”

Section: Video Forensics Overviewmentioning

confidence: 99%

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

El-Shafai,

Fouda,

El-Rabaie

et al. 2023

Multimed Tools Appl

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Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos. Therefore, it’s vital to distinguish between true and fake data. Digital video forgery detection techniques are used to validate and check the realness of digital video content. Deep learning algorithms lately sparked a lot of interest in the field of digital forensics, such as Recurrent Neural Networks (RNN), Deep Convolutional Neural Networks (DCNN), and Adaptive Neural Networks (ANN). In this paper, we give a soft taxonomy as well as a thorough overview of recent research on multimedia falsification detection systems. First, the basic knowledge needed to comprehend video forgery is provided. Then, a summary of active and passive video manipulation detection approaches is provided. Anti-forensics, compression video methods, datasets required for video forensics, and challenges of video detection approaches are also addressed. Following that, we presented an overview of deepfake, and the datasets required for detection were also provided. Also, helpful software packages and forensics tools for video detection are covered. In addition, this paper provides an overview of video analysis tools that are used in video forensic applications. Finally, we highlight research difficulties as well as interesting research avenues. In short, this survey provides detailed information and a broader investigation to extract data and detect fraud video contents under one umbrella.

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Deep Video Forensics: Unveiling Spatial Forgery Detection with Modified ResNet50

Singh,

Rathor,

Kumar

2024

2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT)

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Empirical Evaluation of Motion Cue for Passive-Blind Video Tamper Detection Using Optical Flow Technique

Cited by 3 publications

References 36 publications

DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis

DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

Deep Video Forensics: Unveiling Spatial Forgery Detection with Modified ResNet50

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