Enhancing Image Watermarking With Adaptive Embedding Parameter and PSNR Guarantee

You are here

IEEE Transactions on Multimedia

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Enhancing Image Watermarking With Adaptive Embedding Parameter and PSNR Guarantee

By: 
Ying Huang; Baoning Niu; Hu Guan; Shuwu Zhang

Watermarking plays an important role in identifying the copyright of an image and related issues. The state-of-the-art watermark embedding schemes, spread spectrum and quantization, suffer from host signal interference (HSI) and scaling attacks, respectively. Both of them use a fixed embedding parameter, which is difficult to take both robustness and imperceptibility into account for all images. This paper solves the problems by proposing two novel blind watermarking schemes: a spread spectrum scheme with adaptive embedding strength (SSAES) and a differential quantization scheme with adaptive quantization threshold (DQAQT). Their adaptiveness comes from the proposed adaptive embedding strategy (AEP), which maximizes the embedding strength or quantization threshold by guaranteeing the peak signal-to-noise ratio (PSNR) of the host image after embedding the watermark, and strikes the balance between robustness and imperceptibility. SSAES is HSI free by factoring in the priori knowledge about HSI. In DQAQT, an effective quantization mode is proposed to resist scaling attacks by utilizing the difference between two selected DCT coefficients with high stability. Both SSAES and DQAQT can be easily applied to other watermarking frameworks. We introduce a notion called error threshold to theoretically analyze the performance of our proposed methods in details. The experimental results consistently demonstrate that SSAES and DQAQT outperform the state-of-the-art methods in terms of imperceptibility, robustness, computational cost, and adaptability.

SPS Social Media

IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel