An Adaptive Blind Watermarking Algorithm Based on DCT and Its Application in Big Data

Digital watermarking plays an important role in the big data times. It can be provided copyright protections of digital medias. In this paper, we propose an adaptive blind watermarking algorithm based on compressed DCT and Watson's Visual Mode for Big Data copyright protections. A normalized masking, deduced from Watson's model according to the principle of maximum possible quantization error, is used to simplify the computation for watermark insertion. Hidden information is adaptively embedded based on the deduced masking and relationship of four-adjacent-blocks. Arnold transformation is used to encrypt the watermark signal with a secret key and the watermark is extracted without the original image. Experimental results indicate that the proposed method have strong robustness and high security. And strategy is resistant to Gaussian noise, mean filter, JPEG compression, etc.