Multiple forgery detection in video using inter-frame correlation distance with dual-threshold

Kumar, Vinay; Gaur, Manish

doi:10.1007/s11042-022-13284-2

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…Based on the experimental results carried out, the proposed system has been compared with similar state-of-the-art methods, based on deep learning features [8,21,29,38] and handcrafted features [43,56]. A C3D-based method in Ref.…”

Section: Discussion and Comparison With State-of-the-artmentioning

confidence: 99%

“…Figure 13 compares the accuracy of detecting and identifying tampering types at the video level. Kumar and Gaur [56] reported a detection accuracy of 47% for frame deletion and 77% for frame insertion tampering. They stated that detecting tampering becomes highly complex when only a few frames are deleted from the sequence.…”

Section: Discussion and Comparison With State-of-the-artmentioning

confidence: 99%

“…In contrast, our Model-01 exhibits detection accuracy of 93% and 98.5% for frame deletion and insertion tampering, respectively, while Model-02 achieves detection accuracy of 94% for frame deletion and Figure 13 compares the accuracy of detecting and identifying tampering types at the video level. Kumar and Gaur [56] reported a detection accuracy of 47% for frame deletion and 77% for frame insertion tampering. They stated that detecting tampering becomes highly complex when only a few frames are deleted from the sequence.…”

Section: Discussion and Comparison With State-of-the-artmentioning

confidence: 99%

“…Considering video tampering detection as an anomaly detection task, integrating deep learning techniques with prior information could potentially enhance the efficacy of video tampering detection [55]. Kumar and Gaur [56] proposed a method that extracts deep features using a CNN model. This method establishes the relationship between consecutive frames by calculating the inter-frame correlation coefficient.…”

Section: Literature Reviewmentioning

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: Discussion and Comparison With State-of-the-artmentioning

confidence: 99%

Section: Discussion and Comparison With State-of-the-artmentioning

confidence: 99%

Section: Discussion and Comparison With State-of-the-artmentioning

confidence: 99%

Section: Literature Reviewmentioning

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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“…The forgery operations for digital video can be divided into two groups: inter-frame forgery [1,2] and intra-frame forgery [3]. Intra-frame forgery detection is the main research content of this paper.…”

Section: Introductionmentioning

confidence: 99%

A Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise

Wang

2023

Electronics

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Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, foreground objects of the frame are extracted, and then, reference SPN are trained using frames without foreground objects. Finally, the SPCE is calculated at the block level to distinguish forged objects from normal objects. Experimental results demonstrate that the method can accurately locate the tampered area and has higher detection accuracy. In terms of accuracy and F1-score, our method achieves 0.914 and 0.912, respectively.

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A Simple Difference Based Inter Frame Video Forgery Detection and Localization

Shekar,

Abraham,

Pilar

2024

Communications in Computer and Information Science

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Multiple forgery detection in video using inter-frame correlation distance with dual-threshold

Cited by 9 publications

References 31 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 Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise

A Simple Difference Based Inter Frame Video Forgery Detection and Localization

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