14 research outputs found
Image Steganography: A Review of the Recent Advances
Image Steganography is the process of hiding information which can be text, image or video inside a cover image. The secret information is hidden in a way that it not visible to the human eyes. Deep learning technology, which has emerged as a powerful tool in various applications including image steganography, has received increased attention recently. The main goal of this paper is to explore and discuss various deep learning methods available in image steganography field. Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field
Steganography for Neural Radiance Fields by Backdooring
The utilization of implicit representation for visual data (such as images,
videos, and 3D models) has recently gained significant attention in computer
vision research. In this letter, we propose a novel model steganography scheme
with implicit neural representation. The message sender leverages Neural
Radiance Fields (NeRF) and its viewpoint synthesis capabilities by introducing
a viewpoint as a key. The NeRF model generates a secret viewpoint image, which
serves as a backdoor. Subsequently, we train a message extractor using
overfitting to establish a one-to-one mapping between the secret message and
the secret viewpoint image. The sender delivers the trained NeRF model and the
message extractor to the receiver over the open channel, and the receiver
utilizes the key shared by both parties to obtain the rendered image in the
secret view from the NeRF model, and then obtains the secret message through
the message extractor. The inherent complexity of the viewpoint information
prevents attackers from stealing the secret message accurately. Experimental
results demonstrate that the message extractor trained in this letter achieves
high-capacity steganography with fast performance, achieving a 100\% accuracy
in message extraction. Furthermore, the extensive viewpoint key space of NeRF
ensures the security of the steganography scheme.Comment: 6 pages, 7 figure