PATCH-BASED ADAPTIVE IMAGE AND VIDEO WATERMARKING SCHEMES ON HANDHELD MOBILE DEVICES

Abstract. Mobile devices provide a huge amount of multimedia information sending to the members of social groups every day. Sometimes it is required to authorize the sending information using the limited computational resources of smartphones, tablets or laptops. The hardest problem is with smartphones, which have the limited daily energy and battery life. There are two scenarios for using mobile watermarking techniques. The first scenario is to implement the embedding and extraction schemes using proxy server. In this case, the watermarking scheme does not differ from conventional techniques, including the advanced ones based on adaptive paradigms, deep learning, multi-level protection, and so on. The main issue is to hide the embedding and extracting information from the proxy server. The second scenario is to provide a pseudo-optimized algorithm respect to robustness, imperceptibility and capacity using limited mobile resources. In this paper, we develop the second approach as a light version of adaptive image and video watermarking schemes. We propose a simple approach for creating a patch-based set for watermark embedding using texture estimates in still images and texture/motion estimates in frames that are highly likely to be I-frames in MPEG notation. We embed one or more watermarks using relevant large-sized patches according to two main criteria: high texturing in still images and high texturing/non-significant motion in videos. The experimental results confirm the robustness of our approach with minimal computational costs.

[1]  Lior Wolf,et al.  PatchBatch: A Batch Augmented Loss for Optical Flow , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Tae-Sun Choi,et al.  Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification , 2012, Pattern Recognit. Lett..

[3]  Margarita N. Favorskaya,et al.  Detecting Relevant Regions for Watermark Embedding in Video Sequences Based on Deep Learning , 2020, KES-IDT.

[4]  Margarita N. Favorskaya,et al.  Study of digital textual watermarking distortions under Internet attacks in high resolution videos , 2020, KES.

[5]  Hongyuan Zha,et al.  Unsupervised learning of optical flow with patch consistency and occlusion estimation , 2020, Pattern Recognit..

[6]  Shervan Fekri Ershad,et al.  Texture image analysis and texture classification methods - A review , 2019, ArXiv.

[7]  Qiuqi Ruan,et al.  Adaptive Watermarking and Performance Analysis Based on Image Content , 2007, Int. J. Wavelets Multiresolution Inf. Process..

[8]  Margarita N. Favorskaya,et al.  Authentication and Copyright Protection of Videos Under Transmitting Specifications , 2019 .

[9]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Musab Ghadi,et al.  A joint spatial texture analysis/watermarking system for digital image authentication , 2017, 2017 IEEE International Workshop on Signal Processing Systems (SiPS).

[11]  Margarita N. Favorskaya,et al.  Texture analysis in watermarking paradigms , 2017, KES.

[12]  Musab Ghadi,et al.  A blind spatial domain-based image watermarking using texture analysis and association rules mining , 2018, Multimedia Tools and Applications.

[13]  Liang-Gee Chen,et al.  Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results , 2006, J. VLSI Signal Process..

[14]  Adam Finkelstein,et al.  The Generalized PatchMatch Correspondence Algorithm , 2010, ECCV.

[15]  Raphaël Couturier,et al.  Using Deep learning for image watermarking attack , 2021, Signal Process. Image Commun..

[16]  Nahum Kiryati,et al.  Coarse to over-fine optical flow estimation , 2007, Pattern Recognit..

[17]  Jitendra Malik,et al.  Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.