Conference Papers

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    Video tamper detection techniques based on DCT-SVD and multi-level SVD
    (Institute of Electrical and Electronics Engineers Inc., 2016) Dabhade, A.V.; Bhople, Y.J.; Chandrasekaran, K.; Bhattacharya, S.
    The videos are widely used nowadays for different purposes. But due to easily available software tools, it has become very easy to modify the videos. The videos sent from one end to other can be tampered maliciously in between. The frames in the video can be edited or the sequence of the frames can be altered or even some frames can be deleted, any such malicious alteration is possible. Thus it is very necessary to verify integrity of video data to ensure trustworthiness of the information content. In many cases, such as surveillance, medical, forensic investigations it is necessary to consider authenticity of the video. If there is any tampering in the video, it must be detected. Therefore, there is need to do some work for developing such tamper detection system so that information in a video can be verified. In this paper we propose mechanisms for detecting any such tampering in a video. © 2015 IEEE.
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    Survey: Neural Network Authentication and Tampering Detection
    (Springer, 2023) Kumar, R.; P, A.; Naveen, B.; Chandavarkar, B.R.
    Neural networks have become quite the buzzword in a decade, resulting in extensive research and extensive integration of neural networks in application development. From self-driving vehicles to IoT devices, each such area has seen some form of integration of a neural network(s). Image and video content have found application in medical, forensic, etc. Due to the excessive use of digital content, there has also been a rise in various advanced image editing applications such as Photoshop, making it easier for people to tamper with images. Therefore, coming up with techniques to validate or authenticate images has gained much interest in recent times. Current neural network-based methods can see all kinds of tampering because neural network capability extracts complex features from the images, making them more effective. Thus, in this study, we review some image forgery techniques and look over how neural networks find their application to detect forgery and authenticate images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.