This paper presents a fast algorithm to detect the least significant bit (LSB) steganography. Our approach is inspired by the work of Dumitrescu et al. [1] who detected LSB steganography via sample pair analysis. Our new algorithm combines with the statistical measures developed in [1] and a new least square estimation. The motivation comes from the high accuracy and robustness of the least square method used in parameter estimation. Plentiful experimental results show that our novel method has much lower false alarm rate of 5% than that of 13.79% in [1]. Meanwhile, the estimating precision of our algorithm is about 9% higher than that of the algorithm [1] if the embedding ratio is less than 10%, and the speed of our algorithm is also about 15% faster than the algorithm [1]. Some theoretical derivations are also included.

An improved sample pairs method for detection of LSB embedding

Outsourcing chaotic selective image encryption to the cloud with steganography

A novel design of multiple image encryption using perturbed chaotic map

In recent years, the rapid growth of information technology and digital communication has become very important to secure information transmission between the sender and receiver. Therefore, steganography introduces strongly to hide information and to communicate a secret data in an appropriate multimedia carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has been proposed to hide a large amount of secret data presented by secret color image. This algorithm is based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach has been applied before embedding process. The number of bit to be replaced at each byte is non uniform, it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity reached to four bits per byte.

/pdf/a-new-image-steganography-algorithm-based-on-mlsb-method-yaj0swk6ho.pdf

A New Image Steganography Algorithm Based on MLSB Method with Random Pixels Selection

https://kar.kent.ac.uk/45303/1/Microsoft_Word_-_Stegdetect_article_-_Final.pdf

A study on the false positive rate of Stegdetect

This paper proposes a new method of 2LSB embedding steganography in still images. The proposed method considers a single mismatch in each 2LSB embedding between the 2LSB of the pixel value and 2-bits of the secret message, while the 2LSB replacement overwrites the 2LSB of the image's pixel value with 2-bits of the secret message. The number of bit-changes needed for the proposed method is 0.375 bits from the pixel values of the cover image, which is less than the 0.5 bits of 2LSB replacement. It also reduces the effect of 2LSB embedding pattern of change, which results in lower probability of detection by 44% according to the experimental results.

/pdf/single-mismatch-2lsb-embedding-steganography-2divbk8pz6.pdf

Single-mismatch 2LSB embedding steganography

Unlike other security methods, steganography hides the very existence of secret messages rather than their content only. Both steganography and steganalysis are strongly related to each other, the new steganographic methods should be evaluated with current steganalysis methods and vice-versa. Since steganography is considered broken when the stego object is recognised, undetectability would be the most important property of any steganographic system. Digital image files are excellent media for steganography, as they have redundancy in their representation. Also, the most widely used method of image steganography is the least significant bit (LSB) embedding.
This thesis investigates the latest methods of pixel domain steganography and provides new efficient approaches to improve them in three perspectives: embedding, detection, and the digital forensics investigation process. Firstly, the probability of detection is considered for non-adaptive LSB and 2LSB image steganography even for the embedding rate of 1. The proposed method noticeably reduced the probability of detection for different detection methods via improving the embedding efficiency of both LSB and 2LSB methods, which is not restricted to a specific steganalysis attack.
The extensions to LSB steganography methods have received great attention from steganographers, especially 2LSB, because it is easy to implement, has a higher capacity, is visually imperceptible, brings complex changes to the image pixel values and is harder to detect. The proposed method improves the detection accuracy of the current state of the art targeted 2LSB steganalysis methods via a novel approach pixel value grouping and statistical analysis of the image pixel values histogram. Moreover, a discrete classifier version of the proposed method is developed which gives a label (‘Stego’ or ‘Clean’) to the analysed image and avoids the overhead of setting a right threshold value.
The last perspective of this research considers the evaluation process of the steganalysis tools and simplifying the digital forensics investigation process. Hence, a novel statistical method is proposed to effectively simplify the investigation process by showing the area of differences between the testing image set and the random set of images that is used as a baseline. It also indicates whether the difference is significant or not.
All the above mentioned novel approaches included in this thesis are proven, in both theoretical and practical perspectives, to be better than the current state-of-the-art methods and add some value to the knowledge in the field of steganography, steganalysis and its applications.

New methods to improve the pixel domain steganography, steganalysis, and simplify the assessment of steganalysis tools

In this paper, we propose a new method of non-adaptive LSB steganography in still images toimprove the embedding efficiency from 2 to 8/3 random bits per one embedding change even forthe embedding rate of 1 bit per pixel. The method takes 2-bits of the secret message at a timeand compares them to the LSBs of the two chosen pixel values for embedding, it always assumesa single mismatch between the two and uses the second LSB of the first pixel value to hold theindex of the mismatch. It is shown that the proposed method outperforms the security of LSBreplacement, LSB matching, and LSB matching revisited by reducing the probability ofdetection with their current targeted steganalysis methods. Other advantages of the proposedmethod are reducing the overall bit-level changes to the cover image for the same amount ofembedded data and avoiding complex calculations. Finally, the new method results in littleadditional distortion in the stego image, which could be tolerated.

LSB steganography with improved embedding efficiency and undetectability

In this paper, we propose an extended pairs of values analysis to detect and estimate the amount of secret messages
embedded with 2LSB replacement in digital images based on chi-square attack and regularity rate in pixel values. The
detection process is separated from the estimation of the hidden message length, as it is the main requirement of any
steganalysis method. Hence, the detection process acts as a discrete classifier, which classifies a given set of images into
stego and clean classes. The method can accurately detect 2LSB replacement even when the message length is about
10% of the total capacity, it also reaches its best performance with an accuracy of higher than 0.96 and a true positive
rate of more than 0.997 when the amount of data are 20% to 100% of the total capacity. However, the method puts no
assumptions neither on the image nor the secret message, as it tested with two sets of 3000 images, compressed and
uncompressed, embedded with a random message for each case. This method of detection could also be used as an
automated tool to analyse a bulk of images for hidden contents, which could be used by digital forensics analysts in their
investigation process.

/pdf/detecting-2lsb-steganography-using-extended-pairs-of-values-589cj9n9jn.pdf

Detecting 2LSB steganography using extended pairs of values analysis

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Omed Khalind

Author Tools

Chat about Author