CNN based localisation of forged region in object‐based forgery for HD videos

Kohli, Aditi; Gupta, Abhinav; Singhal, Divya

doi:10.1049/iet-ipr.2019.0397

Cited by 18 publications

(18 citation statements)

References 28 publications

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“…The method has significant accuracy; however, there is a need for cross validation to ensure the generalization. Aditi et al [114] developed a spatiotemporal video forgery detection and localization technique based on CNN. Video frames are detected as tampered or authentic using temporal CNN; latterly, the forgery in video frames is located using spatial CNN.…”

Section: Methods Based On Deep Learningmentioning

confidence: 99%

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

et al. 2022

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Digital videos are now low-cost, easy to capture and easy to share on social media due to the common feature of video recording in smart phones and digital devices. However, with the advancement of video editing tools, videos can be tampered (forged) easily for propaganda or to gain illegal advantages—ultimately, the authenticity of videos shared on social media cannot be taken for granted. Over the years, significant research has been devoted to developing new techniques for detecting different types of video tampering. In this paper, we offer a detailed review of existing passive video tampering detection techniques in a systematic way. The answers to research questions prepared for this study are also elaborated. The state-of-the-art research work is analyzed extensively, highlighting the pros and cons and commonly used datasets. Limitations of existing video forensic algorithms are discussed, and we conclude with research challenges and future directions.

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Section: Methods Based On Deep Learningmentioning

confidence: 99%

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

et al. 2022

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“…Because the motion residual contains both intra-and inter-frame inherent properties of the corresponding frame, it is used as a primal feature for object analysis. Therefore, motion residual is a commonly used feature to detect video forgery in several studies [17,21,23,24]. In this section, we briefly review the motion residual and propose a symmetrically overlapped motion residual to improve the frame identification performance.…”

Section: Symmetrically Overlapped Motion Residualmentioning

confidence: 99%

“…Yao et al [23] extracted high-dimensional features based on high-frequency signals from motion residual features and used CNN to determine whether the frames were tampered with. Kohli et al [24] proposed a spatiotemporal method using CNN to detect video tampering and to localize the forged region in a forged frame. They employed the motion residual to train the presented network.…”

Section: Introductionmentioning

confidence: 99%

“…Motion residual was widely used as a key feature to detect object-based video forgery [17,[23][24][25]. For each video frame, the motion residual contains a substantial number of the intra-frame properties of that frame as well as the inter-frame inherent properties of the corresponding frame.…”

Section: Introductionmentioning

confidence: 99%

“…Most past studies [21,23,25] performed two-class identification, which classifies whether the video frame has been tampered with, to detect object-based video forgery. In [24], another two-class identification is defined by identifying whether the frame has been forged or double-compressed. Only the method of Chen et al [17] reported the three-class identification results.…”

Section: Introductionmentioning

confidence: 99%

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Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual

2022

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Image and video manipulation has been actively used in recent years with the development of multimedia editing technologies. However, object-based video tampering, which adds or removes objects within a video frame, is posing challenges because it is difficult to verify the authenticity of videos. In this paper, we present a novel object-based frame identification network. The proposed method uses symmetrically overlapped motion residuals to enhance the discernment of video frames. Since the proposed motion residual features are generated on the basis of overlapped temporal windows, temporal variations in the video sequence can be exploited in the deep neural network. In addition, this paper introduces an asymmetric network structure for training and testing a single basic convolutional neural network. In the training process, two networks with an identical structure are used, each of which has a different input pair. In the testing step, two types of testing methods corresponding to two- and three-class frame identifications are proposed. We compare the identification accuracy of the proposed method with that of the existing methods. The experimental results demonstrate that the proposed method generates reasonable identification results for both two- and three-class forged frame identifications.

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Towards general object-based video forgery detection via dual-stream networks and depth information embedding

Jin

Wang

et al. 2021

Multimed Tools Appl

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CNN based localisation of forged region in object‐based forgery for HD videos

Cited by 18 publications

References 28 publications

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual

Towards general object-based video forgery detection via dual-stream networks and depth information embedding

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