A Study on Content Tampering in Multimedia Watermarking

• Published in: SN computer science Vol. 4; no. 3; p. 222
• Main Authors: Sahu, Aditya Kumar, Umachandran, Krishnan, Biradar, Vaishali D., Comfort, Olebara, Sri Vigna Hema, V., Odimegwu, Frank, Saifullah M. A
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.05.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Cameras; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Defocusing; Design; Digital signatures; Digitization; Editing; Information Systems and Communication Service; Intellectual property; Internet; Metadata; Multimedia; Occlusion; Original Research; Pattern Recognition and Graphics; Property rights; Retouching; Software; Software Engineering/Programming and Operating Systems; Surveillance; Vision; Deep learning; Tampering investigation; Cyber security; Artificial intelligence; Intrusion detection
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01657-1

Technological progresses offer more occasions for tampering outbreaks. Lithography services at affordable prices, in aggregation with open software tools to influence changes in digital spaces, invigorated amateurs to oblige to fabricating and imitating. Tampering has reached a level of cleverness that leaves negligible trace with the pace of progress happening with editing technology, luring the next generation. However tampering with technology violates intellectual property rights that can be treated to cost dearly, including severe retributions. At the same time, accountability for evading, aiding the evasion of technology, and restricting access that could impede the infringement of the content are new protection activities, which were not present during the pre-digital age. Digitalization shrinks the cost of content development, nevertheless availability of pirated content is also on the rise due to ease of copy, transform and distribution. Surveillance is a big dataset, with arrangements from various sources at diverse scenarios that are critical to events, therefore, susceptible errors such as defocusing, occlusion and displacement. The forensic study is thus correlated, for prediction using multi-task joint model through convolutional neural network (CNN), as they are open to access in metadata, also its alteration through ease of EXIF tools provide innumerable opportunities to tamper contents, thus tough to identify, except after severe inquiries. In this study, we present a brief overview of recent status with respect to the content tampering using several advanced tools.