# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
###### Abstract  
The just noticeable distortion (JND) model plays an important role in measuring the visual visibility for spread transform dither modulation (STDM) watermarking. However, the existing JND model characterizes the suprathreshold distortions with an equal saliency level. Visual saliency (VS) has been widely studied by psychologists and computer scientists during the last decade, where the distortions are more likely to be noticeable to any viewer. With this consideration, we proposed a novel STDM watermarking method for a monochrome image by exploiting a visual saliency-based JND model. In our proposed JND model, a simple VS model is employed as a feature to reflect the importance of a local region and compute the final JND map. Extensive experiments performed on the classic image databases demonstrate that the proposed watermarking scheme works better in terms of the robustness than other related methods. w _information_[2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30],  
rate-distortion-robustness trade-off, it is necessary to take the characteristic of the human visual system (HVS) into consideration. Various STDM-based watermarking algorithms incorporate the perceptual knowledge of HVS to improve fidelity by regulating the watermark strength [6]. The just noticeable distortion (JND) represents distortion perceived by the HVS and determine the watermark strength. In 2006, [PERSON] et al. [7] first proposed a new STDM watermarking using [PERSON]'s perceptual JND model [8]. In their framework, the projection vectors used in STDM are assigned as the slacks computed by [PERSON]'s perceptual model, so as to ensure that more changes are directed to coefficients with larger perceptual slacks. However, it is not robust enough for a volumetric scaling attack. Subsequently, an improved method was proposed [9], where the perceptual model is not only used to determine the projection vector but also used to select the quantization step size. However, it must use many DCT coefficients for one-bit embedding and a low embedding rate can be resulted. In [10], an additional block classification based contrast masking and luminance adaptation was considered by [PERSON] for digital images. A spatial JND model proposed by [PERSON] [11] incorporates new spatial contrast sensitivity function (CSF), luminance adaptation, and contrast masking. [PERSON] proposed to compute the quantization step size by adopting the projection vector and the perceptual slacks from [PERSON]'s JND model [12]. Based on the luminance effect, which was only part of [PERSON]'s model, [PERSON] et al [13] proposed a step-projection based scheme that can ensure the values of quantization step size used in the watermark embedder and detector. More recently, [PERSON] [14] presented an improved STDM watermarking scheme based on a more sophisticated luminance-based JND model [15]. Recently, the visual saliency (VS) model has been attracting tremendous attention [16]. As a consequence of evolution, most vertebrates, including humans, have a remarkable ability to automatically pay more attention to salient regions of the visual scene. As we all know, perceptual watermarking should take full advantage of the results from human visual system (HVS) studies. JND gives us a way to model the HVS accurately. VS is another very important aspect affecting human perception. Intuitively, VS and JND are intrinsically related because both of them depend on how the HVS perceives an image and suprathreshold distortions can be a strong attractor of visual attention [17]. Visual saliency can enhance or reduce actual visual sensitivity. Consequently, JND needs to be adjusted to the salient areas in images. Salient areas are generally regarded as the attention focus in human eyes.