Data hiding using integer lifting wavelet transform and DNA computing

DNA computing widely used in encryption or hiding the data. Many researchers have proposed many developments of encryption and hiding algorithms based on DNA sequence to provide new algorithms. In this paper data hiding using integer lifting wavelet transform based on DNA computing is presented. The transform is applied on blue channel of the cover image. The DNA encoding used to encode the two most significant bits of LL sub-band. The produced DNA sequence used for two purpose, firstly, it use to construct the key for encryption the secret data and secondly to select the pixels in HL, LH, HH sub-bands for hiding in them. Many measurement parameters used to evaluate the performance of the proposed method such PSNR, MSE, and SSIM. The experimental results show high performance with respect to different embedding rate

Generating and Validating DSA Private Keys from Online Face Images for Digital Signatures

Signing digital documents is attracting more attention in recent years, according to the rapidly growing number of digital documents being exchanged online. The digital signature proves the authenticity of the document and the sender’s approval on the contents of the document. However, storing the private keys of users for digital signing imposes threats toward gaining unauthorized access, which can result in producing false signatures. Thus, in this paper, a novel approach is proposed to extract the private component of the key used to produce the digital signature from online face image. Hence, this private component is never stored in any database, so that, false signatures cannot be produced and the sender’s approval cannot be denied. The proposed method uses a convolutional neural network that is trained using a semi-supervised approach, so that, the values used for the training are extracted based on the predictions of the neural network. To avoid the need for training a complex neural network, the proposed neural network makes use of existing pretrained neural networks, that already have the knowledge about the distinctive features in the faces. The use of the MTCNN for face detection and Facenet for face recognition, in addition to the proposed neural network, to achieved the best performance. The performance of the proposed method is evaluated using the Colored FERET Faces Database Version 2 and has achieved robustness rate of 13.48% and uniqueness of 100%

An enhanced hybrid image encryption algorithm using Rubik’s cube and dynamic DNA encoding techniques

Image encryption is among the most active solutions to protect confidential pictorial information. However, to design a strong image encryption algorithm with no recognizable pattern, the researchers in this field have to enrich the confusion and diffusion properties. This study proposes an efficient hybrid system that combines two techniques. First, we propose a modified version of Rubik's Cube technique for scrambling colored image pixels to achieve fast confusion. This technique not only scrambles the position of image pixels but also scrambles the color channels. Then, dynamic DNA encoding algorithm is used to encrypt the pixel’s values. DNA encoding rules are used in conjunction with a secret key. We propose to select the DNA rules dynamically to enhance the security level. Five fidelity metrics are employed to assess the capability of this system. These are PSNR, SSIM, NPCR, Entropy, and CCA. The results indicate that the proposed system enhances the general security requirements with enriched confusion and diffusion properties of the encrypted image

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population