Visual Attention-Based Image Watermarking

Abstract

Imperceptibility and robustness are two complementary but fundamental requirements of any watermarking algorithm. Low strength watermarking yields high imperceptibility but exhibits poor robustness. High strength watermarking schemes achieve good robustness but often infuse distortions resulting in poor visual quality in host media. If distortion due to high strength watermarking can avoid visually attentive regions, such distortions are unlikely to be noticeable to any viewer. In this paper, we exploit this concept and propose a novel visual attention-based highly robust image watermarking methodology by embedding lower and higher strength watermarks in visually salient and non-salient regions, respectively. A new low complexity wavelet domain visual attention model is proposed that allows us to design new robust watermarking algorithms. The proposed new saliency model outperforms the state-of-the-art method in joint saliency detection and low computational complexity performances. In evaluating watermarking performances, the proposed blind and non-blind algorithms exhibit increased robustness to various natural image processing and filtering attacks with minimal or no effect on image quality, as verified by both subjective and objective visual quality evaluation. Up to 25% and 40% improvement against JPEG2000 compression and common filtering attacks, respectively, are reported against the existing algorithms that do not use a visual attention model.

Power efficient dataflow design for a heterogeneous smart camera architecture

“…Salient objects are not size specific therefore Multi-Resolution Analysis (MRA) is adopted within many models (e.g., [14]). Multiple orientation and MRA in the discrete wavelet transform (DWT) closely resembles human vision and maximise simultaneous localisation in both the spatial and frequency domains.…”

“…Multiple orientation and MRA in the discrete wavelet transform (DWT) closely resembles human vision and maximise simultaneous localisation in both the spatial and frequency domains. As a result it is used in saliency computation and shows good promise in reducing complexity while achieving superior performance [14]. This algorithm uses a multi-resolution DWT as its core decomposition and therefore is a more suitable choice than other FPGA saliency implementations [15], [16] that naively follow algorithm implementations not amenable to FPGAs, e.g., by relying on external memory access [15] or by utilizing more computationally complex hardware implementations of calculations [16], such as the Gabor oriented-filter or convolution with smoothing filter.…”

Graph spectral domain watermarking for unstructured data from sensor networks

“…Magnitude based multiplicative watermarking widely forms as the basis for non-blind watermarking [15] [16]. We embed the watermark in to the graph Fourier coefficientsf as follows:…”

A Watermarking Algorithm Based on DCT and JPEG Quantization Table

“…Digital watermarking technology is an effective way to solve this problem, and it is able to protect information security, achieve security traceability and copyright protection [1][2][3][4]. Some identification information (digital watermarking) is directly or indirectly embedded into the digital carrier, and does not only affect the value of the original carrier, but is also uneasy to be detected or modified.…”