SCReedSolo: A Secure and Robust LSB Image Steganography Framework with Randomized Symmetric Encryption and Reed-Solomon Coding
- URL: http://arxiv.org/abs/2503.12368v1
- Date: Sun, 16 Mar 2025 06:01:05 GMT
- Title: SCReedSolo: A Secure and Robust LSB Image Steganography Framework with Randomized Symmetric Encryption and Reed-Solomon Coding
- Authors: Syed Rifat Raiyan, Md. Hasanul Kabir,
- Abstract summary: We introduce $rm SCRsmall EEDSsmall OLO$, a novel framework for concealing arbitrary binary data within images.<n>We show that our framework achieves a data payload of 3 bits per pixel for an RGB image, and mathematically assess the probability of successful transmission.
- Score: 0.9208007322096533
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Image steganography is an information-hiding technique that involves the surreptitious concealment of covert informational content within digital images. In this paper, we introduce ${\rm SCR{\small EED}S{\small OLO}}$, a novel framework for concealing arbitrary binary data within images. Our approach synergistically leverages Random Shuffling, Fernet Symmetric Encryption, and Reed-Solomon Error Correction Codes to encode the secret payload, which is then discretely embedded into the carrier image using LSB (Least Significant Bit) Steganography. The combination of these methods addresses the vulnerability vectors of both security and resilience against bit-level corruption in the resultant stego-images. We show that our framework achieves a data payload of 3 bits per pixel for an RGB image, and mathematically assess the probability of successful transmission for the amalgamated $n$ message bits and $k$ error correction bits. Additionally, we find that ${\rm SCR{\small EED}S{\small OLO}}$ yields good results upon being evaluated with multiple performance metrics, successfully eludes detection by various passive steganalysis tools, and is immune to simple active steganalysis attacks. Our code and data are available at https://github.com/Starscream-11813/SCReedSolo-Steganography.
Related papers
- Halton Scheduler For Masked Generative Image Transformer [51.82285573627426]
Masked Generative Image Transformers (MaskGIT) have emerged as a scalable and efficient image generation framework.
We analyze the sampling objective in MaskGIT, based on the mutual information between tokens.
We propose a new sampling strategy based on our Halton scheduler instead of the original Confidence scheduler.
arXiv Detail & Related papers (2025-03-21T12:00:59Z) - Image Encryption Using DNA Encoding, Snake Permutation and Chaotic Substitution Techniques [0.7743851353380347]
This paper presents a new image encryption scheme using DNA encoding, snake permutation and chaotic substitution techniques.
DNA encoding and snake permutation modules ensure effective scrambling of the pixels.
For the confusion part, the chaotic substitution technique is implemented, which substitutes the pixel values chosen randomly from 3 S-boxes.
arXiv Detail & Related papers (2025-03-12T03:54:37Z) - DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion Models [38.17146643777956]
Coverless image steganography (CIS) enhances imperceptibility by not using any cover image.
Recent works have utilized text prompts as keys in CIS through diffusion models.
We propose DiffStega, an innovative training-free diffusion-based CIS strategy for universal application.
arXiv Detail & Related papers (2024-07-15T06:15:49Z) - Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection [13.840950434728533]
State-of-the-art Synthetic Image Detection (SID) research has led to strong evidence on the advantages of feature extraction from foundation models.
We leverage the image representations extracted by intermediate Transformer blocks of CLIP's image-encoder via a lightweight network.
Our method is compared against the state-of-the-art by evaluating it on 20 test datasets and exhibits an average +10.6% absolute performance improvement.
arXiv Detail & Related papers (2024-02-29T12:18:43Z) - Recoverable Privacy-Preserving Image Classification through Noise-like
Adversarial Examples [26.026171363346975]
Cloud-based image related services such as classification have become crucial.
In this study, we propose a novel privacypreserving image classification scheme.
encrypted images can be decrypted back into their original form with high fidelity (recoverable) using a secret key.
arXiv Detail & Related papers (2023-10-19T13:01:58Z) - Human-imperceptible, Machine-recognizable Images [76.01951148048603]
A major conflict is exposed relating to software engineers between better developing AI systems and distancing from the sensitive training data.
This paper proposes an efficient privacy-preserving learning paradigm, where images are encrypted to become human-imperceptible, machine-recognizable''
We show that the proposed paradigm can ensure the encrypted images have become human-imperceptible while preserving machine-recognizable information.
arXiv Detail & Related papers (2023-06-06T13:41:37Z) - Expressive Losses for Verified Robustness via Convex Combinations [67.54357965665676]
We study the relationship between the over-approximation coefficient and performance profiles across different expressive losses.
We show that, while expressivity is essential, better approximations of the worst-case loss are not necessarily linked to superior robustness-accuracy trade-offs.
arXiv Detail & Related papers (2023-05-23T12:20:29Z) - 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) - MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust
Classifier [37.774220727662914]
We propose a one-shot mask-guided image synthesis that allows controlling manipulations of a single image.
Our proposed method, entitled MAGIC, leverages structured gradients from a pre-trained quasi-robust classifier.
MAGIC aggregates gradients over the input, driven by a guide binary mask that enforces a strong, spatial prior.
arXiv Detail & Related papers (2022-09-23T12:15:40Z) - Inverse Problems Leveraging Pre-trained Contrastive Representations [88.70821497369785]
We study a new family of inverse problems for recovering representations of corrupted data.
We propose a supervised inversion method that uses a contrastive objective to obtain excellent representations for highly corrupted images.
Our method outperforms end-to-end baselines even with a fraction of the labeled data in a wide range of forward operators.
arXiv Detail & Related papers (2021-10-14T15:06:30Z) - Deep Reinforcement Learning with Label Embedding Reward for Supervised
Image Hashing [85.84690941656528]
We introduce a novel decision-making approach for deep supervised hashing.
We learn a deep Q-network with a novel label embedding reward defined by Bose-Chaudhuri-Hocquenghem codes.
Our approach outperforms state-of-the-art supervised hashing methods under various code lengths.
arXiv Detail & Related papers (2020-08-10T09:17:20Z) - Modeling Lost Information in Lossy Image Compression [72.69327382643549]
Lossy image compression is one of the most commonly used operators for digital images.
We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
arXiv Detail & Related papers (2020-06-22T04:04:56Z)
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.