Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment

Vinolin, V.; Sucharitha, M.

doi:10.1007/s00371-020-01992-5

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…In this layer, the feature maps produced using the conv layers are downsampled to control the spatial size and data. 24 Fully connected layers: The feature maps of the convolutional layer are given to the fully connected layer, where the final processing is executed for the generation of the output as…”

Section: Max Pooling Layermentioning

confidence: 99%

“…In this section, the intra‐frame forgery detection using the proposed raven‐finch‐based deep CNN classifier is presented. Generally, the deep CNN classifier 24 assists the classification without the need for human intervention, while the classification accuracy of the deep CNN classifier relies on the effective training of the classifier's internal modal parameters. Normally, Adam optimization is used for tuning the deep classifiers due to the adaptive tuning and minimal memory requirements, but the convergence to the global optimal solution is a challenge with a higher tendency towards the local optimal convergence phenomenon.…”

Section: Proposed Raven‐finch Optimization Tuned Deep Cnn For Intra‐f...mentioning

confidence: 99%

“…Max pooling layer: In this layer, the feature maps produced using the conv layers are downsampled to control the spatial size and data 24 …”

Section: Proposed Raven‐finch Optimization Tuned Deep Cnn For Intra‐f...mentioning

confidence: 99%

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Raven finch optimized deep convolutional neural network model for intra‐frame video forgery detection

Singla

Singh

Nagpal

2022

Concurrency and Computation

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SUMMARY Due to the tremendous growth of video editing software, it has become extremely simple to introduce malicious content by manipulating multimedia data. This may include modification of videos either by adding or deleting selective frames with malicious intentions. Hence, it is essential to find the forged frames of the videos by introducing efficient and reliable video forensic methods. This article presents an automatic intra‐frame video forgery detection strategy based on a hybrid optimization tuned deep‐convolutional neural network (deep‐CNN) classifier. The significance of the proposed method lies in developing the proposed raven‐finch optimization algorithm that tunes the weights of the deep‐CNN to exhibit enhanced detection accuracy. The proposed raven‐finch optimization algorithm combines raven search agents and finches search agents, possessing the benefits of both search agents. The features of the input video frames act as the input to the deep‐CNN classifier that detects forgery. The performance of the proposed raven‐finch‐based deep CNN method is analyzed in terms of the performance indices, such as accuracy, sensitivity, and specificity. It is attained to be 97.56%, 95.48%, and 96.38%, respectively, which shows the superiority of the proposed method for intra‐frame forgery detection.

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Section: Max Pooling Layermentioning

confidence: 99%

Section: Proposed Raven‐finch Optimization Tuned Deep Cnn For Intra‐f...mentioning

confidence: 99%

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Raven finch optimized deep convolutional neural network model for intra‐frame video forgery detection

Singla

Singh

Nagpal

2022

Concurrency and Computation

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“…Vinolin et al. [2] focus on establishing the 3D model of the video frame to generate light coefficients in order to detect the forgeries in videos. Chen et al.…”

Section: Introductionmentioning

confidence: 99%

“…Tyagi et al [1] provide a detailed analysis of image and video manipulation and detection techniques. Vinolin et al [2] focus on establishing the 3D model of the video frame to generate light coefficients in order to detect the forgeries in videos. Chen et al [3] propose a blind detection model for image forensics based on weak feature extraction.…”

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confidence: 99%

FPC‐Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG‐Loss

Chen

et al. 2022

IET Computer Vision

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With the rapid development of manipulation technologies, the generation of Deep Fake videos is more accessible than ever. As a result, face forgery detection becomes a challenging task, attracting a significant amount of attention from researchers worldwide. However, most previous work, consisting of convolutional neural networks (CNN), is not sufficiently discriminative and cannot fully utilise subtle clues and similar textures during the process of facial forgery detection. Moreover, these methods cannot simultaneously consider accuracy and time efficiency. To address such problems, we propose a novel framework named FPC-Net to extract some meaningful and unnatural expressions in local regions. This framework utilises CNN, long short-term memory (LSTM), channel groups loss (CG-Loss) and adaptive feature fusion to detect face forgery videos. First, the proposed method exploits spatial features by CNN, and a channel-wise attention mechanism is employed to separate channels. Specifically, with the help of channel groups loss, the channels are divided into two groups, each representing a specific class. Second, LSTM is applied to learn the correlation of spatial features. Finally, the correlation of features is mapped into other latent spaces. Through a lot of experiments, the results are that the detection speed of the proposed method reaches 420 FPS and the auc scores achieve best performance of 99.7%, 99.9%, 94.7%, and 82.0% on Raw Celeb-DF, Raw Face Forensics++, F2F and NT datasets respectively. The experimental results demonstrate that the proposed framework has great time efficiency performance while improving the detection performance compared with other frame-level methods in most cases. K E Y W O R D S adaptive feature fusion, facial forgery detection | INTRODUCTIONSince video synthesis has made remarkable progress and multimedia technologies have witnessed an explosion of generative models of continuously growing capability and capacity, the malicious abuse of video manipulation technology is causing great concern. Many scholars are devoted to the research of video and image forgery detection and have achieved some achievements. Tyagi et al. [1] provide a detailed analysis of image and video manipulation and detection techniques. Vinolin et al. [2] focus on establishing the 3D model of the video frame to generate light coefficients in order to detect the forgeries in videos. Chen et al. [3] propose a blind detection model for image forensics based on weak feature extraction. However, the videos generated by generative adversarial networks (GANs) [4] or variational autoencoder (VAEs) [5] are too realistic to distinguish, which causes serious problems, such as fake news, public security and privacy threats. For example, in 2018, a realistic-looking video showed that the former President Barack Obama was cussing another former President, Donald Trump, bringing attention to the risk of Deep-Fake. Recently, the most popular term 'DeepFake' in video This is an open access article under the terms of the Crea...

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A novel data hiding by image interpolation using edge quad-tree block complexity

Roselinkiruba¹,

Sharmila²

2021

Vis Comput

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Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment

Cited by 18 publications

References 23 publications

Raven finch optimized deep convolutional neural network model for intra‐frame video forgery detection

Raven finch optimized deep convolutional neural network model for intra‐frame video forgery detection

FPC‐Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG‐Loss

A novel data hiding by image interpolation using edge quad-tree block complexity

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