Traceable content protection based on chaos and neural networks

In this paper, a media content encryption/decryption algorithm is designed based on a chaos system and neural networks, which generates random sequences with chaos, and encrypts or decrypts media contents with neural networks in a parallel way. In this scheme, different decryption keys can be used to recover the media content into different copies. That is, the decryption operation gets the content containing certain random sequence that can be used as the identification. With respect to this property, the scheme is used for secure content distribution. Taking the audio content for example, it is encrypted by a key at the sender side and decrypted by different keys at the receiver side. The differences between decryption keys lead to different decrypted audio copies. If one customer distributes his copy to other unauthorized customers, the chaotic sequence contained in the copy can tell the illegal customer. The performances, including security, imperceptibility and robustness, are analyzed, and some experimental results are given to show the scheme's practicability.