Roberto Caldelli
ABSTRACT
From the very beginning when photography appeared and then much more in the digital era, images and videos have been edited not only to improve the visual quality of what they represented but also to change what had been acquired to mystify the reality. This is often done in order to transfer a different meaning to the watcher and basically mislead his/her opinion. On the other side, during the years, the need for even more effective defense instruments able to detect such alterations has increased. This has become particularly crucial with the advent of deep-learning based techniques that have allowed to rather easily achieve realistic results in content manipulation (deepfakes) but also in multimedia synthetic generation. This talk will provide a look throughout the evolution of the various kinds of manipulations with a parallel focus on the diverse multimedia forensic techniques and approaches [1, 2]. An analysis will be carried out to try to understand how needs and solutions have been evolved [3] in order to fix the lesson learnt and to individuate future research challenges.
- Irene Amerini, Lamberto Ballan, Roberto Caldelli, Alberto Del Bimbo, and Giuseppe Serra. 2011. A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery. IEEE Transactions on Information Forensics and Security 6, 3 (2011), 1099–1110. https://doi.org/10.1109/TIFS.2011.2129512Google Scholar
Digital Library
- Hany Farid. 2009. Image forgery detection. IEEE Signal Processing Magazine 26, 2 (2009), 16–25. https://doi.org/10.1109/MSP.2008.931079Google ScholarCross Ref
- Luisa Verdoliva. 2020. Media Forensics and DeepFakes: An Overview. IEEE Journal of Selected Topics in Signal Processing 14, 5 (2020), 910–932. https://doi.org/10.1109/JSTSP.2020.3002101Google ScholarCross Ref
Index Terms
- Multimedia Forensics versus disinformation in images and videos: lesson learnt and new challenges
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