@article{Jamshidi_Tabatabaee_Akhaee_2024, title={Performance Analysis of an Image Watermarking Approach Based on Sample Rotation with Wavelet Transformation}, volume={3}, url={https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/performance-analysis-of-an-image-watermarking-approach-based-on-sample-rotation-with-wavelet-transformation/}, DOI={10.1234/mjee.v3i3.79}, abstractNote={ This paper presents a simple watermarking approach based on the rotation of low frequency components in image blocks. The rotation process is performed with less distortion by projecting the samples on specific lines according to their message bit. To optimize the detection, the Maximum Likelihood criteria have been used. Thus by computing the distribution of rotated noisy samples, the optimum decoder is presented and its performance is analytically investigated. The privilege of this proposed algorithm is its inherent robustness against gain attack as well as its simplicity. Experimental results confirm the validity of the analytical derivations and also its high robustness against common attacks.     }, number={3}, journal={Majlesi Journal of Electrical Engineering}, publisher={OICC Press}, author={Jamshidi, Azizollah and Tabatabaee, Sajad and Akhaee, Ali}, year={2024}, month={Feb.}, keywords={Watermarking, fa, Wavelet Transformation, ML Detector, performance analysis.} }