High payload watermarking based on enhanced image saliency detection

Abstract

Nowadays, images are circulated rapidly over the internet and they are subject to some risk of misuses. To address this issue, various watermarking methods are proposed in the literature. However, most conventional methods achieve a certain trade-off among imperceptibility and high capacity payload, and they are not able to improve these criteria simultaneously. Therefore, in this paper, a robust saliency-based image watermarking method is proposed to achieve high payload and high quality watermarked image. First, an enhanced salient object model is proposed to produce a saliency map, followed by a binary mask to segments the foreground/background region of a host image. The same mask is then consulted to decompose the watermark image. Next, the RGB channels of the watermark are encrypted by using Arnold, 3-DES and multi-flipping permutation encoding (MFPE). Furthermore, the principal key used for encryption is embedded in the singular matrix of the blue channel. Moreover, the blue channel is encrypted by using the Okamoto-Uchiyama homomorphic encryption (OUHE) method. Finally, these encrypted watermark channels are diffused and embedded into the host channels. When the need arises, more watermarks can be embedded into the host at the expense of the quality of the embedded watermarks. Our method can embed watermark of the same dimension as the host image, which is the first of its kind. Experimental results suggest that the proposed method maintains robustness while achieving high image quality and high payload. It also outperforms the state-of-the-art (SOTA) methods.

HDR image watermarking using saliency detection and quantization index modulation

“…where i refers to the i th feature channel, CON V is the convolution with value Θ, and is the concatenation operator. The binary mask S B is produced by thresholding S [5]. The loss function is L(S, g) = (S log g)+(1−S) log(1−g), where g is the ground truth.…”

“…In analyzing the aforementioned methods, it is evident that capacity is sacrificed in an attempt to improve the other two aspects of quality and robustness [5,6,[8][9][10][11][12][13][14][15][16][17][18]. To simultaneously achieve high embedding capacity, image quality, and robustness, one approach is to leverage on different saliency regions of an image for a watermarking process [5,19]. This can be achieved by using salient object detection (SoD) to extract the salient regions of an image to embed the watermark.…”