Visual Attention-Based Image Watermarking
Visual Attention-Based Image Watermarking
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DOI：
10.1109/access.2016.2627241
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发表时间：
2016-11
期刊：
影响因子：
3.9
通讯作者：
Deepayan Bhowmik;Matthew Oakes;Charith Abhayaratne
中科院分区：
文献类型：
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作者：
Deepayan Bhowmik;Matthew Oakes;Charith Abhayaratne
不可感知性和鲁棒性是任何水印算法的两个互补但基本的要求。低强度水印具有较高的不可感知性，但鲁棒性较差。高强度的水印算法具有很好的鲁棒性，但往往会引入失真，导致宿主媒体的视觉质量较差。如果由于高强度水印而导致的失真可以避免视觉上注意的区域，则这样的失真不太可能被任何观看者注意到。在本文中，我们利用这一概念，并提出了一种新的视觉注意力为基础的高鲁棒性的图像水印方法，通过嵌入低和高强度的水印在视觉显着和非显着区域，分别。提出了一种新的低复杂度的小波域视觉注意力模型，使我们能够设计新的鲁棒水印算法。提出的新显著性模型在联合显著性检测和低计算复杂度性能方面优于最先进的方法。在评估水印的性能，所提出的盲和非盲算法表现出更强的鲁棒性，以各种自然图像处理和滤波攻击，对图像质量的影响很小或没有，验证了主观和客观的视觉质量评价。对JPEG2000压缩和常见的过滤攻击，分别高达25%和40%的改善，对现有的算法，不使用视觉注意力模型。
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.