Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright. The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification. Some of these features are important perceptual features according to the human visual system (HVS), which means that the embedded watermark should be imperceptible in these features. Therefore, both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions. The two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks, like volumetric scaling, histogram qualization, and non-conventional watermarking attacks like the Denoising Convolution Neural Network (DnCNN), which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art attacks. The DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm (CSRWA) to prove its robustness. For this algorithm to satisfy the robustness and imperceptibility tradeoff, implementing the Dither Modulation (DM) algorithm is boosted by utilizing the Just Noticeable Distortion (JND) principle to get an improved performance in this sense. Sensitivity, luminance, inter and intra-block contrast are used to adjust the JND values.

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