Steganography for Neural Radiance Fields by Backdooring
- URL: http://arxiv.org/abs/2309.10503v1
- Date: Tue, 19 Sep 2023 10:27:38 GMT
- Title: Steganography for Neural Radiance Fields by Backdooring
- Authors: Weina Dong, Jia Liu, Yan Ke, Lifeng Chen, Wenquan Sun, Xiaozhong Pan,
- Abstract summary: We propose a novel model steganography scheme with implicit neural representation.
The NeRF model generates a secret viewpoint image, which serves as a backdoor.
We train a message extractor using overfitting to establish a one-to-one mapping between the secret message and the secret viewpoint image.
- Score: 6.29495604869364
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- See then Tell: Enhancing Key Information Extraction with Vision Grounding [54.061203106565706]
We introduce STNet (See then Tell Net), a novel end-to-end model designed to deliver precise answers with relevant vision grounding.
To enhance the model's seeing capabilities, we collect extensive structured table recognition datasets.
arXiv Detail & Related papers (2024-09-29T06:21:05Z) - Cover-separable Fixed Neural Network Steganography via Deep Generative Models [37.08937194546323]
We propose a Cover-separable Fixed Neural Network Steganography, namely Cs-FNNS.
In Cs-FNNS, we propose a Steganographic Perturbation Search (SPS) algorithm to directly encode the secret data into an imperceptible perturbation.
We demonstrate the superior performance of the proposed method in terms of visual quality and undetectability.
arXiv Detail & Related papers (2024-07-16T05:47:06Z) - Image steganography based on generative implicit neural representation [2.2972561982722346]
This paper proposes an image steganography based on generative implicit neural representation.
By fixing a neural network as the message extractor, we effectively redirect the training burden to the image itself.
The accuracy of message extraction attains an impressive mark of 100%.
arXiv Detail & Related papers (2024-06-04T03:00:47Z) - 3D Visibility-aware Generalizable Neural Radiance Fields for Interacting
Hands [51.305421495638434]
Neural radiance fields (NeRFs) are promising 3D representations for scenes, objects, and humans.
This paper proposes a generalizable visibility-aware NeRF framework for interacting hands.
Experiments on the Interhand2.6M dataset demonstrate that our proposed VA-NeRF outperforms conventional NeRFs significantly.
arXiv Detail & Related papers (2024-01-02T00:42:06Z) - Hiding Functions within Functions: Steganography by Implicit Neural Representations [9.630341407412729]
We propose StegaINR to implement steganography.
StegaINR embeds a secret function into a stego function, which serves as both the message extractor and the stego media.
To our knowledge, this is the first work to introduce INR into steganography.
arXiv Detail & Related papers (2023-12-07T22:55:48Z) - StegaNeRF: Embedding Invisible Information within Neural Radiance Fields [61.653702733061785]
We present StegaNeRF, a method for steganographic information embedding in NeRF renderings.
We design an optimization framework allowing accurate hidden information extractions from images rendered by NeRF.
StegaNeRF signifies an initial exploration into the novel problem of instilling customizable, imperceptible, and recoverable information to NeRF renderings.
arXiv Detail & Related papers (2022-12-03T12:14:19Z) - Hiding Images in Deep Probabilistic Models [58.23127414572098]
We describe a different computational framework to hide images in deep probabilistic models.
Specifically, we use a DNN to model the probability density of cover images, and hide a secret image in one particular location of the learned distribution.
We demonstrate the feasibility of our SinGAN approach in terms of extraction accuracy and model security.
arXiv Detail & Related papers (2022-10-05T13:33:25Z) - Deniable Steganography [30.729865153060985]
Steganography conceals the secret message into the cover media, generating a stego media which can be transmitted on public channels without drawing suspicion.
As its countermeasure, steganalysis mainly aims to detect whether the secret message is hidden in a given media.
We propose a receiver-deniable steganographic scheme to deal with the receiver-side coercive attack using deep neural networks (DNN)
arXiv Detail & Related papers (2022-05-25T09:00:30Z) - NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance
Fields [54.27264716713327]
We show that a Neural Radiance Fields (NeRF) representation of a scene can be used to train dense object descriptors.
We use an optimized NeRF to extract dense correspondences between multiple views of an object, and then use these correspondences as training data for learning a view-invariant representation of the object.
Dense correspondence models supervised with our method significantly outperform off-the-shelf learned descriptors by 106%.
arXiv Detail & Related papers (2022-03-03T18:49:57Z) - Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis [86.38901313994734]
We present DietNeRF, a 3D neural scene representation estimated from a few images.
NeRF learns a continuous volumetric representation of a scene through multi-view consistency.
We introduce an auxiliary semantic consistency loss that encourages realistic renderings at novel poses.
arXiv Detail & Related papers (2021-04-01T17:59:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.