PCPT and ACPT: Copyright Protection and Traceability Scheme for DNN Models

Deep neural networks (DNNs) have achieved tremendous success in artificial intelligence (AI) fields. However, DNN models can be easily illegally copied, redistributed, or abused by criminals, seriously damaging the interests of model inventors. The copyright protection of DNN models by neural network watermarking has been studied, but the establishment of a traceability mechanism for determining the authorized users of a leaked model is a new problem driven by the demand for AI services. Because the existing traceability mechanisms are used for models without watermarks, a small number of false-positives are generated. Existing black-box active protection schemes have loose authorization control and are vulnerable to forgery attacks. Therefore, based on the idea of black-box neural network watermarking with the video framing and image perceptual hash algorithm, a passive copyright protection and traceability framework PCPT is proposed that uses an additional class of DNN models, improving the existing traceability mechanism that yields a small number of false-positives. Based on an authorization control strategy and image perceptual hash algorithm, a DNN model active copyright protection and traceability framework ACPT is proposed. This framework uses the authorization control center constructed by the detector and verifier. This approach realizes stricter authorization control, which establishes a strong connection between users and model owners, improves the framework security, and supports traceability verification

An Optimized Medical Image Watermarking Approach for E-Health Applications

Background: In recent years, information and communication technologies have been widely used in the healthcare sector. This development enables E-Health applications to transmit medical data, as well as their sharing and remote access by healthcare professionals. However, due to their sensitivity, medical data in general, and medical images in particular, are vulnerable to a variety of illegitimate attacks. Therefore, suitable security and effective protection are necessary during transmission. Method: In consideration of these challenges, we put forth a security system relying on digital watermarking with the aim of ensuring the integrity and authenticity of medical images. The proposed approach is based on Integer Wavelet Transform as an embedding algorithm; furthermore, Particles Swarm Optimization was employed to select the optimal scaling factor, which allows the system to be compatible with different medical imaging modalities. Results: The experimental results demonstrate that the method provides a high imperceptibility and robustness for both secret watermark and watermarked images. In addition, the proposed scheme performs better for medical images compared with similar watermarking algorithms. Conclusion: As it is suitable for a lossless-data application, IWT is the best choice for medical images integrity. Furthermore, using the PSO algorithm enables the algorithm to be compatible with different medical imaging modalities

Image Steganography using Hybrid Edge Detector and Ridgelet Transform

Steganography is the art of hiding high sensitive information in digital image, text, video, and audio. In this paper, authors have proposed a frequency domain steganography method operating in the Ridgelet transform. Authors engage the advantage of ridgelet transform, which represents the digital image with straight edges. In the embedding phase, the proposed hybrid edge detector acts as a preprocessing step to obtain the edge image from the cover image, then the edge image is partitioned into several blocks to operate with straight edges and Ridgelet transform is applied to each block. Then, the most significant gradient vectors (or significant edges) are selected to embed the secret data. The proposed method has shown the advantages of imperceptibility of the stego image is increased because the secret data is hidden in the significant gradient vector. Authors employed the hybrid edge detector to obtain the edge image, which increases the embedding capacity. Experimental results demonstrates that peak signal-to-noise (PSNR) ratio of stego image generated by this method versus the cover image is guaranteed to be above 49 dB. PSNR is much higher than that of all data hiding techniques reported in the literature.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.214-219, DOI: http://dx.doi.org/10.14429/dsj.65.787

Imperceptible Image Watermarking based on Chinese Remainder Theorem over the Edges

This paper introduced a watermarking method using the CRT and Canny Algorithm that able to improve the imperceptibility of watermarked image and preserving the robustness of watermark image as well. The classical CRT algorithm is spread the watermark bits evenly on the image area. It causes significant degradation when the embedding location lies on the least significant region or in the homogeny area. Otherwise, the proposed method embeds the watermark on the edges of the image which have significant difference value to maintain the imperceptibility. The Canny algorithm is used to indexing the embedding location based on the filtering output of host image. The watermark is then embedded into the host image using pair-wise coprime integers of 6 and 11 within the CRT modulo. The results show that the proposed method has significant improvement in the quality of watermarked image with the average value of 0.9995 compared to the CRT method which results in value of 0.9985. In compression and additive noise attacks the CRT has average values of 0.6618 and 0.9750, while the proposed method results in similar values of 0.6616 and 0.9752 respectively. These prove that the proposed method is able to preserve the robustness while improving the imperceptibility