MPEG Double Compression Based Intra-Frame Video Forgery Detection using CNN

Bakas, Jamimamul; Bashaboina, Anil Kumar; Naskar, Ruchira

doi:10.1109/icit.2018.00053

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…Bakas et al in [95] presented a forensic solution to detect and localize double compressionbased forgery in MPEG videos by exploiting its I-frames. They introduced CNN architecture that exploits the fact that double compression introduces specific artifacts in the DCT coefficients of the I-frames of an MPEG video.…”

Section: Methods Based On Compressionmentioning

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 Compressionmentioning

confidence: 99%

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

et al. 2022

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“…Video forgery detection is mainly categorized as inter-frame forgery and copy-move forgery Copying and pasting content within the same frame is referred to as copy-move forgery [6][7][8]. Copying and pasting content within the same frame is referred to as copy-move forgery.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Deep learning methods and specifically Convolution Neural Networks (CNNs) have gained tremendous success due to its powerful ability of automatic learning of features for large-scale video classification [7,9,10]. Copy move forgery problem is investigated a lot, however, inter-frame duplication is not explored much and still is not applicable in real-time due to computational limitations and robustness issues for real-time scenarios.…”

Section: Introductionmentioning

confidence: 99%

Duplicate Frame Video Forgery Detection Using Siamese-based RNN

Munawar¹,

Noreen²

2021

Intelligent Automation &Amp; Soft Computing

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Video and image data is the most important and widely used format of communication today. It is used as evidence and authenticated proof in different domains such as law enforcement, forensic studies, journalism, and others. With the increase of video applications and data, the problem of forgery in video and images has also originated. Although a lot of work has been done on image forgery, video forensic is still a challenging area. Videos are manipulated in many ways. Frame insertion, deletion, and frame duplication are a few of the major challenges. Moreover, in the perspective of duplicated frames, frame rate variation and loop detection are also key issues. Identification of forged duplication frames for large videos with variant frame rates in real-time is not applicable due to computational limitations, lack of generalization, and low-performance accuracy. This research has investigated the problem of frame duplication with varied frame rates using a deep learning approach. A novel deep learning framework consisting of Inflated 3D (I3D) and Siamese-based Recurrent Neural Network (RNN) is proposed to resolve the aforementioned issues. The first step in the proposed framework is to extract the features and convert videos into frames. I3D network receives an original and a forged video to detect frame-to-frame duplication. Then multiple frames are merged to create a sequence. This sequence is passed to Siamese-based RNN which is used for the sequence to sequence forgery detection in video. Media Forensic Challenge (MFC) is a relatively new dataset with various frame rates, and a huge volume of videos. MFC and Image Retrieval and Analysis Tool (VIRAT) datasets are used for training and validation of the proposed model. The accuracy of the proposed method with the VIRAT dataset is 86.6% and with the MFC dataset 93%. The comparative analysis with stateof-the-art approaches has shown the robustness of the proposed approach.

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“…In 2018, Bakas et al proposed a deep learning architecture which utilizes artifacts in the I-frames to detect double quantization. They used TRACE library for their comparisons [27]. [28] constructs their method using I3D and Siamens network to detect video forgery operation.…”

Section: Introductionmentioning

confidence: 99%

Video Forgery Detection Method based on Local Difference Binary

Ulutaş¹,

Üstübi̇oğlu²,

Ulutaş³

et al. 2020

Pamukkale J Eng Sci

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Öz Recently, the rapid development of video editing software has made video forgery applicable. Researchers have proposed methods to detect forged video frames. These methods utilize codec properties, motion artifacts, noise effect and frame similarity to detect forgery. Execution time and low detection accuracy are the two main drawbacks of forgery detection methods reported in the literature. In this study, a new frame duplication detection method using Local Difference Binary (LDB) is proposed to extract features from the frames. Distance between similar frames that have similar feature vectors are is used by the method to estimate Distance of Forgery and to determine the exact location of duplicated frames. PSNR between similar frames are is then used to group them into three classes, and rule-based mechanism reports forged frames according to the membership to classes. Experimental results indicate that the proposed method has lower execution time with higher accuracy than similar works. Son yıllarda, video düzenleme yazılımının hızlı gelişimi video sahteciliğini uygulanabilir hale getirmiştir. Araştırmacılar sahte video karelerini tespit etmek için yöntemler önermiştir. Bu yöntemler sahteciliği tespit etmek için kodek özelliklerini, hareket artefaktlarını, gürültü etkisini ve çerçeve benzerliğini kullanmaktadır. Literatürde sahtecilik tespiti için önerilen yöntemlerin iki ana dezavantajı çalışma zamanı ve düşük tespit doğruluğudur. Bu çalışmada, çerçevelerden özellikler çıkarmak için Yerel Fark İkililerini (LDB) kullanan yeni bir çerçeve tekrarlama tespit yöntemi önerilmiştir. Benzer özellik vektörlerine sahip benzer çerçeveler arasındaki mesafe, Sahtecilik Mesafesini tahmin etmek ve kopyalanan çerçevelerin tam yerini belirlemek için kullanılmaktadır. Benzer çerçeveler arasındaki PSNR daha sonra bunları üç sınıfa gruplamak için kullanılır ve kural tabanlı mekanizma sahte çerçeveleri sınıf üyeliklerine göre raporlar. Deneysel sonuçlar, önerilen yöntemin, benzer çalışmalara kıyasla daha yüksek hassasiyetle daha düşük uygulama süresine sahip olduğunu göstermektedir.

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MPEG Double Compression Based Intra-Frame Video Forgery Detection using CNN

Cited by 9 publications

References 15 publications

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

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

Duplicate Frame Video Forgery Detection Using Siamese-based RNN

Video Forgery Detection Method based on Local Difference Binary

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