scholarly journals Reference-free differential histogram-correlative detection of steganography: performance analysis

2022 ◽  
Vol 25 (1) ◽  
pp. 329
Author(s):  
Natiq M. Abdali ◽  
Zahir M. Hussain
Keyword(s):  
Neural Networks ◽  
Performance Analysis ◽  
Recent Work ◽  
Detection Method ◽  
Spatial Domain ◽  
Image Steganography ◽  
Least Significant Bit ◽  
Stego Image ◽  
Image Tampering ◽  
Derivatives Of

<span lang="EN-US">Recent <span>research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless </span> modified.</span>

scholarly journals A Safe and Secured Medical Textual Information Using an Improved LSB Image Steganography

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Roseline Oluwaseun Ogundokun ◽  
Oluwakemi Christiana Abikoye
Keyword(s):  
Medical Information ◽  
Medical Data ◽  
Image Steganography ◽  
Medical Information System ◽  
Least Significant Bit ◽  
Stego Image ◽  
Performance Metric ◽  
Challenging Tasks ◽  
Multimedia Technologies ◽  
Lsb Technique

Safe conveyance of medical data across unsecured networks nowadays is an essential issue in telemedicine. With the exponential growth of multimedia technologies and connected networks, modern healthcare is a huge step ahead. Authentication of a diagnostic image obtained from a specialist at a remote location which is from the sender is one of the most challenging tasks in an automated healthcare setup. Intruders were found to be able to efficiently exploit securely transmitted messages from previous literature since the algorithms were not efficient enough leading to distortion of information. Therefore, this study proposed a modified least significant bit (LSB) technique capable of protecting and hiding medical data to solve the crucial authentication issue. The application was executed and established by utilizing MATLAB 2018a, and it used a logical bit shift operation for execution. The investigational outcomes established that the proposed technique can entrench medical information without leaving a perceptible falsification in the stego image. The result of this implementation shows that the modified LSB image steganography outperformed the standard LSB technique with a higher PSNR value and lower MSE value when compared with previous research works. The number of shifts was added as a new performance metric for the proposed system. The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches.

scholarly journals Steganography using Spatial Domain Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 3369-3373
Keyword(s):  
Data Security ◽  
Data Transfer ◽  
Spatial Domain ◽  
Secret Message ◽  
Least Significant Bit ◽  
Secret Data ◽  
Stego Image ◽  
Hiding Capacity ◽  
Military Organization ◽  
Redundant Data

In present world data transfer using the internet is growing. It is very easy and fast way to transfer information like confidential documents, economic transactions, business applications and other covert information over internet. With the advent and growth of internet, people are more concerned about security of information. Data Security is important while data is transferred over internet because any illegal user can access important and private data also make it worthless. Research in data security area will help government agencies, military organization and private companies to securely transmit their confidential data over internet. From past few years various steganography techniques have been developed to hide secret message using various multimedia objects having large amount of redundant data to support steganography. In this paper introduction about steganography, related concepts and implementation of commonly used spatial domain techniques like LSB(Least Significant Bit Technique) with modulus, PVD(Pixel Value Difference) with LSB replacement and adaptive data hiding over edges with LSB are considered. It is observed(while visual, statistical analysis and experiments were carried out) with benchmark cover and stego objects that embedding same amount of secret data in each pixel leads to more visible distortions in a stego image because all pixels do not bear same amount of changes and this effect is more observed in smooth area then edges. Improving stego image imperceptibility and adjusting hiding capacity adaptively are major related research challenges about spatial domain techniques.

scholarly journals Multi-Edge Concept used for Image Steganography

2019 ◽  
Vol 7 (1) ◽  
pp. 1-12
Author(s):  
Rasber Dh. Rashid ◽  
Ladeh S. Abdulrahman ◽  
Taban F. Majeed
Keyword(s):  
Spatial Domain ◽  
Jpeg Compression ◽  
Image Steganography ◽  
Stego Image ◽  
Sensitive Data ◽  
Digital Steganography

Digital Steganography means hiding sensitive data inside a cover object ina way that is invisible to un-authorized persons. Many proposed steganography techniques in spatial domain may achieve high invisibility requirement but sacrifice the good robustness against attacks. In some cases, weneed to take in account not just the invisibility but also we need to thinkabout other requirement which is the robustness of recovering the embedded secrete messages. In this paper we propose a new steganoraphicscheme that aims to achieve the robustness even the stego image attackedby steganalyzers. Furthermore, we proposed a scheme which is more robust against JPEG compression attack compared with other traditionalsteganography schemes.

Multilevel Secure Digital Image Steganography Framework Using Random Function and Advanced Encryption Standard

2019 ◽  
Vol 16 (11) ◽  
pp. 4812-4825
Author(s):  
Mohsin N. Srayyih Almaliki
Keyword(s):  
Image Quality ◽  
Random Function ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Secret Message ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Stego Image ◽  
Preparation Phase ◽  
Noise Ratio

One of the crucial aspects of processes and methodologies in the information and communication technology era is the security of information. The security of information should be a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies which are used, and they include steganography and cryptography. With cryptography, the secret message is converted into unintelligible text, but the existence of the secret message is noticed, nonetheless, steganography involves hiding the secret message in a way that its presence cannot be noticed. In this paper, a new secure image steganography framework which is known as an adaptive stego key LSB (ASK-LSB) framework is proposed. The construction of the proposed framework was carried out in four phases with the aim of improving the data-hiding algorithm in cover images by using capacity, image quality, and security. To achieve this, the Peak Signal-to-Noise Ratio (PSNR) of the steganography framework was maintained. The four phases began with the image preparation phase, followed by the secret message preparation phase, embedding phase and finally extraction phase. The secure image steganography framework that is proposed in this study is based on a new adaptive of least significant bit substitution method, combination random function, and encryption method. In the proposed work, the secret bits are inserted directly or inversely, thereby enhancing the imperceptibility and complexity of the process of embedding. Results from the experiment reveal that the algorithm has better image quality index, peak signal-to-noise ratio, and payload used in the evaluation of the stego image.

scholarly journals High Capacity Image Steganography using Pixel Value Differencing Method with Data Compression using Neural Network

2019 ◽  
Vol 8 (12) ◽  
pp. 1800-1804
Keyword(s):  
Neural Network ◽  
Data Compression ◽  
Data Hiding ◽  
Image Steganography ◽  
Data Embedding ◽  
Least Significant Bit ◽  
Secret Data ◽  
Stego Image ◽  
Compression Technique ◽  
Hiding Capacity

The Digital Market Is Rapidly Growing Day By Day. So, Data Hiding Is Going To Increase Its Importance. Information Can Be Hidden In Different Embedding Mediums, Known As Carriers By Using Steganography Techniques. The Carriers Are Different Multimedia Medium Such As Images, Audio Files, Video Files, And Text Files .There Are Several Techniques Present To Achieve Data Hiding Like Least Significant Bit Insertion Method And Transform Domain Technique. The Data Hidden Capacity Inside The Cover Image Totally Depends On The Properties Of The Image Like Number Of Noisy Pixels. Data Compression Provides To Hide Large Amount Of Secret Data To Increase The Capacity And The Image Steganography Based On Any Neural Network Provides That The Size And Quality Of The Stego-Image Remains Unaltered After Data Embedding. In This Paper We Propose A New Method Combined With Data Compression Along With Data Embedding Technique And After Embedding To Maintain The Quality The Communication Channel Use The Neural Network. The Compression Technique Increase The Data Hiding Capacity And The Use Of Neural Network Maintain The Flow Of Data Processing Signal

scholarly journals The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs

2020 ◽  
Vol 2020 (4) ◽  
pp. 119-1-119-7
Author(s):  
Xinwei Zhao ◽  
Matthew C. Stamm
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Recent Work ◽  
Convolutional Neural Networks ◽  
Image Manipulation ◽  
Tampering Detection ◽  
Future Design ◽  
Image Tampering ◽  
Image Tampering Detection ◽  
The One

In recent years, convolutional neural networks (CNNs) have been widely used by researchers to perform forensic tasks such as image tampering detection. At the same time, adversarial attacks have been developed that are capable of fooling CNN-based classifiers. Understanding the transferability of adversarial attacks, i.e. an attacks ability to attack a different CNN than the one it was trained against, has important implications for designing CNNs that are resistant to attacks. While attacks on object recognition CNNs are believed to be transferrable, recent work by Barni et al. has shown that attacks on forensic CNNs have difficulty transferring to other CNN architectures or CNNs trained using different datasets. In this paper, we demonstrate that adversarial attacks on forensic CNNs are even less transferrable than previously thought even between virtually identical CNN architectures! We show that several common adversarial attacks against CNNs trained to identify image manipulation fail to transfer to CNNs whose only difference is in the class definitions (i.e. the same CNN architectures trained using the same data). We note that all formulations of class definitions contain the unaltered class. This has important implications for the future design of forensic CNNs that are robust to adversarial and anti-forensic attacks.

Sign in / Sign up

Export Citation Format

Share Document