Comparative Performance Analysis Of Deep Learning-Based Image Steganography Using U-Net, V-Net, And U-Net++ Encoders

Digital Imaging steganography is the act of hiding information in a cover picture in a way that can't be found or recovered. Three main types of methods are used in digital image steganography: neural network methods, spatial methods, and transform methods. The pixel values of an image are changed by spatial methods to embed information. On the other hand, the frequency of the image is changed by transform methods to embed information that is hidden. There are methods that use neural networks to hide things, and this is what the suggested method is all about. Through digital image steganography, this study looks into how deep convolutional neural networks (CNNs) can be used. With the increasing concerns about data infringement during transmission and storage, image steganography techniques have gained attention for hiding secret information within cover images. Traditional methods suffer from limitations such as low embedding capacity and poor reconstruction quality. To address these challenges, deep learning-based approaches have been proposed in the literature. Among them, the Convolutional Neural Network (CNN) based U-Net encoder has been extensively studied. However, its comparative performance with other CNN-based encoders like V-Net and U-Net++ remains unexplored in the context of image steganography. In this paper, we implement V-Net and U-Net++ encoders for image steganography and conduct a comprehensive performance assessment alongside U-Net architecture. These architectures are utilized to conceal a secret image within a cover image, and a unified and robust decoder is designed to extract the hidden information. Through experimental evaluations, we compare the embedding capacity, stego quality, and reconstruction quality of the three architectures. The U-Net architecture outperforms V-Net and U-Net++ in terms of embedding capacity and the quality of stego and reconstructed secret images. This research provides valuable insights into the effectiveness of different deep learning-based encoders for image steganography applications, aiding in the selection of appropriate architectures for securing digital images against unauthorized access. &nbsp

Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective

The booming interest in adversarial attacks stems from a misalignment between human vision and a deep neural network (DNN), i.e. a human imperceptible perturbation fools the DNN. Moreover, a single perturbation, often called universal adversarial perturbation (UAP), can be generated to fool the DNN for most images. A similar misalignment phenomenon has recently also been observed in the deep steganography task, where a decoder network can retrieve a secret image back from a slightly perturbed cover image. We attempt explaining the success of both in a unified manner from the Fourier perspective. We perform task-specific and joint analysis and reveal that (a) frequency is a key factor that influences their performance based on the proposed entropy metric for quantifying the frequency distribution; (b) their success can be attributed to a DNN being highly sensitive to high-frequency content. We also perform feature layer analysis for providing deep insight on model generalization and robustness. Additionally, we propose two new variants of universal perturbations: (1) Universal Secret Adversarial Perturbation (USAP) that simultaneously achieves attack and hiding; (2) high-pass UAP (HP-UAP) that is less visible to the human eye.Comment: Accepted to AAAI 202

InvVis: Large-Scale Data Embedding for Invertible Visualization

We present InvVis, a new approach for invertible visualization, which is reconstructing or further modifying a visualization from an image. InvVis allows the embedding of a significant amount of data, such as chart data, chart information, source code, etc., into visualization images. The encoded image is perceptually indistinguishable from the original one. We propose a new method to efficiently express chart data in the form of images, enabling large-capacity data embedding. We also outline a model based on the invertible neural network to achieve high-quality data concealing and revealing. We explore and implement a variety of application scenarios of InvVis. Additionally, we conduct a series of evaluation experiments to assess our method from multiple perspectives, including data embedding quality, data restoration accuracy, data encoding capacity, etc. The result of our experiments demonstrates the great potential of InvVis in invertible visualization.Comment: IEEE VIS 202

THInImg: Cross-modal Steganography for Presenting Talking Heads in Images

Cross-modal Steganography is the practice of concealing secret signals in publicly available cover signals (distinct from the modality of the secret signals) unobtrusively. While previous approaches primarily concentrated on concealing a relatively small amount of information, we propose THInImg, which manages to hide lengthy audio data (and subsequently decode talking head video) inside an identity image by leveraging the properties of human face, which can be effectively utilized for covert communication, transmission and copyright protection. THInImg consists of two parts: the encoder and decoder. Inside the encoder-decoder pipeline, we introduce a novel architecture that substantially increase the capacity of hiding audio in images. Moreover, our framework can be extended to iteratively hide multiple audio clips into an identity image, offering multiple levels of control over permissions. We conduct extensive experiments to prove the effectiveness of our method, demonstrating that THInImg can present up to 80 seconds of high quality talking-head video (including audio) in an identity image with 160x160 resolution.Comment: Accepted at WACV 202

Learning Iterative Neural Optimizers for Image Steganography

Image steganography is the process of concealing secret information in images through imperceptible changes. Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natural images, and propose an iterative neural network trained to perform the optimization steps. In contrast to classical optimization methods like L-BFGS or projected gradient descent, we train the neural network to also stay close to the manifold of natural images throughout the optimization. We show that our learned neural optimization is faster and more reliable than classical optimization approaches. In comparison to previous state-of-the-art encoder-decoder-based steganography methods, it reduces the recovery error rate by multiple orders of magnitude and achieves zero error up to 3 bits per pixel (bpp) without the need for error-correcting codes.Comment: International Conference on Learning Representations (ICLR) 202

CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

Neural Radiance Fields (NeRF) have the potential to be a major representation of media. Since training a NeRF has never been an easy task, the protection of its model copyright should be a priority. In this paper, by analyzing the pros and cons of possible copyright protection solutions, we propose to protect the copyright of NeRF models by replacing the original color representation in NeRF with a watermarked color representation. Then, a distortion-resistant rendering scheme is designed to guarantee robust message extraction in 2D renderings of NeRF. Our proposed method can directly protect the copyright of NeRF models while maintaining high rendering quality and bit accuracy when compared among optional solutions.Comment: 11 pages, 6 figures, accepted by iccv 2023 non-camera-ready versio