An Effective Semi-fragile Watermarking Method for Image Authentication Based on Lifting Wavelet Transform and Feed-Forward Neural Network


Digital watermarking is a significant issue in the field of information security and avoiding the misuse of images in the world of Internet and communication. This paper proposes a novel watermarking method for tamper detection and recovery using semi-fragile data hiding, based on lifting wavelet transform (LWT) and feed-forward neural network (FNN). In this work, first, the host image is decomposed up to one level using LWT, and the discrete cosine transform (DCT) is applied to each 2×2 blocks of diagonal details. Next, a random binary sequence is embedded in each block as the watermark by correlating DC coefficients. In the authentication stage, first, the geometry is analyzed by using speeded up robust features (SURF) algorithm and extract watermark bits by using FNN. Afterward, logical exclusive or operation between original and extracted watermark is applied to detect tampered region. Eventually, in the recovery stage, tampered regions are recovered using the inverse halftoning technique. The performance and efficiency of the method and its robustness against various geometric, non-geometric, and hybrid attacks are reported. From the experimental results, it can be seen that the proposed method is superior in terms of robustness and quality of the watermarked and recovered images, respectively, compared to the state-of-the-art methods. Besides, imperceptibility has been improved by using different correlation steps as the gain factor for flat (smooth) and texture (rough) blocks. Based on the advantages exhibited, the proposed method outperforms the related works, in terms of superiority, efficiency, and effectiveness for tamper detection and recovery-based applications.

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Correspondence to Amir Hossein Taherinia.

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Bolourian Haghighi, B., Taherinia, A.H. & Monsefi, R. An Effective Semi-fragile Watermarking Method for Image Authentication Based on Lifting Wavelet Transform and Feed-Forward Neural Network. Cogn Comput 12, 863–890 (2020).

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  • Watermarking
  • Image authentication and restoration
  • Tamper detection and recovery
  • Lifting wavelet transform
  • Halftone technique
  • Feed-forward neural network