Related papers: Reversible Data Hiding over Encrypted Images via I…
The paper proposes a method to secure the Compressive Sensing (CS) streams. It consists in protecting part of the measurements by a secret key and inserting the code into the rest. The secret key is generated via a cryptographically secure…
The rise of digital medical imaging, like MRI and CT, demands strong encryption to protect patient data in telemedicine and cloud storage. Chaotic systems are popular for image encryption due to their sensitivity and unique characteristics,…
In recent years, reversible data hiding has attracted much more attention than before. Reversibility signifies that the original media can be recovered without any loss from the marked media after extracting the embedded message. This paper…
In this paper a general framework to adopt different predictors for reversible data hiding in the encrypted image is presented. We propose innovative predictors that contribute more significantly than conventional ones results in…
To provide an added security level most of the existing reversible as well as irreversible image steganography schemes emphasize on encrypting the secret image (payload) before embedding it to the cover image. The complexity of encryption…
In recent years, several encryption algorithms have been proposed to protect digital images from cryptographic attacks. These encryption algorithms typically use a relatively small key space and therefore, provide safe, especially if they…
Large training data and expensive model tweaking are standard features of deep learning for images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which raises privacy concerns. Existing…
Data hiding is the procedure of encoding desired information into a certain types of cover media (e.g. images) to resist potential noises for data recovery, while ensuring the embedded image has few perceptual perturbations. Recently, with…
Considering the prospects of public key embedding (PKE) mechanism in active forensics on the integrity or identity of ciphertext for distributed deep learning security, two reversible data hiding in encrypted domain (RDH-ED) algorithms with…
This paper introduces Hierarchical Image Steganography, a novel method that enhances the security and capacity of embedding multiple images into a single container using diffusion models. HIS assigns varying levels of robustness to images…
Pixel Value Ordering (PVO) holds an impressive property for high fidelity Reversible Data Hiding (RDH). In this paper, we introduce a dual-PVO (dPVO) for Prediction Error Expansion(PEE), and thereby develop a new RDH scheme to offer a…
A new grayscale-based block scrambling image encryption scheme is presented to enhance the security of Encryption-then-Compression (EtC) systems, which are used to securely transmit images through an untrusted channel provider. The proposed…
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, which pose security challenges to hyperspectral image (HSI) classification based on DNNs. Numerous adversarial attack methods have been designed in the domain of natural…
Ensuring data privacy and protection has become paramount in the era of deep learning. Unlearnable examples are proposed to mislead the deep learning models and prevent data from unauthorized exploration by adding small perturbations to…
This paper presents a new scheme for hiding a secret message in binary images. Given m*n cover image block, the new scheme can conceal as many as log(m*n +1) bits of data in block, by changing at most one bit in the block. The hiding…
Massive human-related data is collected to train neural networks for computer vision tasks. A major conflict is exposed relating to software engineers between better developing AI systems and distancing from the sensitive training data. To…
In this paper, we propose a reversible data hiding method in the spatial domain for compressed grayscale images. The proposed method embeds secret bits into a compressed thumbnail of the original image by using a novel interpolation method…
Image retrieval is crucial in robotics and computer vision, with downstream applications in robot place recognition and vision-based product recommendations. Modern retrieval systems face two key challenges: scalability and efficiency.…
Privacy-preserving deep neural networks (DNNs) have been proposed for protecting data privacy in the cloud server. Although several encryption schemes for visually protection have been proposed for privacy-preserving DNNs, several attacks…
Image security for information has become increasingly critical as internet become more prevalent due to hacking and unauthorized access. To ensure the security of confidential image data, image encryption using visual cryptography plays a…