Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique

With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes

Biometric sensors

A Biometric Sensor is an electronic device that captures raw biometric samples in a form that is suitable for generation of a biometric template, which can further be used for verification or authentication of an individual’s identity. Examples include cameras, computer keyboards, microphones, fingerprint readers and iris scanner

An efficient medical image watermarking scheme based on FDCuT–DCT

In this paper, a blind medical image watermarking scheme based on Fast Discrete Curvelet Transform (FDCuT) and Discrete Cosine Transform (DCT) is proposed. FDCuT is applied on the medical image to get different frequency coefficients of its curvelet decomposition. On the high frequency curvelet coefficients of the medical image, block wise DCT is applied to get different frequency coefficients. Then mid band frequency coefficients of the medical image are modified by White Gaussian Noise (WGN) sequences according to watermark bit to get watermarked medical image. At extraction end, blind recovery of watermark data is performed by correlation of WGN sequences. The proposed scheme is tested for its effectiveness on various types of medical images such as X-ray, Ultrasound (US), Magnetic Resonant Imaging (MRI) and Computerized Tomography (CT). Result analysis shows that imperceptibility of watermarked medical image is better as PSNR is above 45 dB for all types of the medical images. In addition, the robustness of the scheme is better than an existing scheme for a similar set of medical images in terms of Normalized Correlation (NC). Experimental results show that scheme is robust to geometric attacks, signal processing attacks and JPEG compression attacks. An analysis is also carried out to verify the performance of the proposed scheme to support binary watermarks with different details in it: text and logos. Moreover, the proposed scheme resulted in zero false positive rate when tested on 100 non-watermarked images

Challenges of deep learning in medical image analysis – improving explainability and trust

Deep learning has revolutionized the detection of diseases and is helping the healthcare sector break barriers in terms of accuracy and robustness to achieve efficient and robust computer-aided diagnostic systems. The application of deep learning techniques empowers automated AI-based utilities requiring minimal human supervision to perform any task related to medical diagnosis of fractures, tumors, and internal hemorrhage; preoperative planning; intra-operative guidance, etc. But deep learning faces some major threats to the flourishing healthcare domain. This paper traverses the major challenges that the deep learning community of researchers and engineers faces, particularly in medical image diagnosis, like the unavailability of balanced annotated medical image data, adversarial attacks faced by deep neural networks and architectures due to noisy medical image data, a lack of trustability among users and patients, and ethical and privacy issues related to medical data. This study explores the possibilities of AI autonomy in healthcare by overcoming the concerns about trust that society has in autonomous intelligent systems