A Lightweight and Robust APBT–LBP Deep Feature Zero-Watermarking Framework with DNA Encryption for Medical Images

The protection of medical images in healthcare services has become important due to the adoption of telemedicine and cloud-based healthcare services. Conventional watermarking methods embed information directly into the host image, which may introduce subtle distortions and potentially affect diagnostic accuracy. This paper addresses the problem by introducing a robust zero-watermarking scheme that preserves the original medical image without any modification. The proposed approach is designed on a hybrid feature extraction scheme. Initially, desired regions of interest are identified using variance-based analysis. Local Binary Pattern (LBP) is then applied to capture fine texture details. Further, the All-Phase Biorthogonal Transform (APBT) is used to obtain stable low-frequency in-formation. These features are subsequently pipelined through the lightweight VGG16 convolutional neural network to extract high-level semantic representations. The resulting features are fused, normalized, encrypted, and converted into binary form using Quantization Index Modulation (QIM). DNA encryption is applied to the watermark to produce a secure zero-watermark key that is stored externally. Extensive experiments conducted under a wide range of signal processing and geometric attacks show the effectiveness of the proposed method. The results show high normalized correlation above 0.99, bit error rates (≤ 0.1%), and complete preservation of higher image quality, making the approach suitable for medical image authentication and copyright protection.