NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation
Yangping Lin, Yan Ke, Ke Niu, Jia Liu, Xiaoyuan Yang

TL;DR
NeR-VCP introduces an innovative video content protection method utilizing implicit neural representations and key-controllable modules, enhancing security and reducing data transfer through model compression, with superior visual and cryptographic performance.
Contribution
This paper presents a novel automatic video encryption scheme based on implicit neural networks and key-controllable modules, differing from traditional encryption methods.
Findings
Effective encryption of videos using implicit neural networks.
Improved security and imperceptibility against illegal users.
Reduced encrypted data transfer via model compression.
Abstract
With the popularity of video applications, the security of video content has emerged as a pressing issue that demands urgent attention. Most video content protection methods mainly rely on encryption technology, which needs to be manually designed or implemented in an experience-based manner. To address this problem, we propose an automatic encryption technique for video content protection based on implicit neural representation. We design a key-controllable module, which serves as a key for encryption and decryption. NeR-VCP first pre-distributes the key-controllable module trained by the sender to the recipients, and then uses Implicit Neural Representation (INR) with a (pre-distributed) key-controllable module to encrypt plain video as an implicit neural network, and the legal recipients uses a pre-distributed key-controllable module to decrypt this cipher neural network (the…
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Taxonomy
TopicsDigital Media Forensic Detection · Advanced Steganography and Watermarking Techniques · Generative Adversarial Networks and Image Synthesis
