Robust Image Tampering Detection and Ownership Authentication Using Zero-Watermarking and Siamese Neural Networks

The development of advanced image editing tools has significantly increased the manipulation of digital images, creating a pressing need for robust tamper detection and ownership authentication systems. This paper presents a method that combines zero-watermarking with Siamese neural networks to detect image tampering and verify ownership. The approach utilizes features from the Discrete Wavelet Transform (DWT) and employs two halftone images as watermarks: one representing the owner's portrait and the other corresponding to the protected image. A feature matrix is generated from the owner's portrait using the Siamese network and securely linked to the image's halftone watermark through an XOR operation. Additionally, data augmentation enhances the model's robustness, ensuring effective learning of image features even under geometric and signal processing distortions. Experimental results demonstrate high accuracy in recovering halftone images, enabling precise tamper detection and ownership verification across different datasets and image distortions (geometric and image processing distortions).

@article{Arevalo-Ancona2024,
  title     = {Robust Image Tampering Detection and Ownership Authentication Using Zero-Watermarking and Siamese Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Rodrigo Eduardo Arevalo-Ancona and Manuel Cedillo-Hernandez and Francisco Javier Garcia-Ugalde},
  doi       = {10.14569/IJACSA.2024.0151046},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151046}
}