Performance evaluation measurement of image steganography techniques with analysis of LSB based on variation image formats

Recently, Steganography is an outstanding research area which used for data protection from unauthorized access. Steganography is defined as the art and science of covert information in plain sight in various media sources such as text, images, audio, video, network channel etc. so, as to not stimulate any suspicion; while steganalysis is the science of attacking the steganographic system to reveal the secret message. This research clarifies the diverse showing the evaluation factors based on image steganographic algorithms. The effectiveness of a steganographic is rated to three main parameters, payload capacity, image quality measure and security measure. This study is focused on image steganographic which is most popular in in steganographic branches. Generally, the Least significant bit is major efficient approach utilized to embed the secret message. In addition, this paper has more detail knowledge based on Least significant bit LSB within various Images formats. All metrics are illustrated in this study with arithmetical equations while some important trends are discussed also at the end of the paper

A Review on Steganography Techniques

Steganography is the science of hiding a secret message in cover media, without any perceptual distortion of the cover media. Using steganography, information can be hidden in the carrier items such as images, videos, sounds files, text files, while performing data transmission. In image steganography field, it is a major concern of the researchers how to improve the capacity of hidden data into host image without causing any statistically significant modification. Therefore, this paper presents most of the recent works that have been conducted on image steganography field and analyzes them to clarify the strength and weakness points in each work separately in order to be taken in consideration for future works in such field.   

Improved Deep Hiding/Extraction Algorithm to Enhance the Payload Capacity and Security Level of Hidden Information

Steganography algorithms have become a significant technique for preventing illegal users from obtaining secret data. In this paper, a deep hiding/extraction algorithm has been improved (IDHEA) to hide a secret message in colour images. The proposed algorithm has been applied to enhance the payload capacity and reduce the time complexity. Modified LSB (MLSB) is based on disseminating secret data randomly on a cover-image and has been proposed to replace a number of bits per byte (Nbpb), up to 4 bits, to increase payload capacity and make it difficult to access the hiding data. The number of levels of the IDHEA algorithm has been specified randomly; each level uses a colour image, and from one level to the next, the image size is expanded, where this algorithm starts with a small size of a cover-image and increases the size of the image gradually or suddenly at the next level, according to an enlargement ratio. Lossless image compression based on the run-length encoding algorithm and Gzip has been applied to enable the size of the data that is hiding at the next level, and data encryption using the Advanced Encryption Standard algorithm (AES) has been introduced at each level to enhance the security level. Thus, the effectiveness of the proposed IDHEA algorithm has been measured at the last level, and the performance of the proposed hiding algorithm has been checked by many statistical and visual measures in terms of the embedding capacity and imperceptibility. Comparisons between the proposed approach and previous work have been implemented; it appears that the intended approach is better than the previously modified LSB algorithms, and it works against visual and statistical attacks with excellent performance achieved by using the detection error (PE). Furthermore, the results confirmed that the stego-image with high imperceptibility has reached even a payload capacity that is large and replaces twelve bits per pixel (12-bpp). Moreover, testing is confirmed in that the proposed algorithm can embed secret data efficiently with better visual quality

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

Pokročilé metody detekce steganografického obsahu

Steganography can be used for illegal activities. It is essential to be prepared. To detect steganography images, we have a counter-technique known as steganalysis. There are different steganalysis types, depending on if the original artifact (cover work) is known or not, or we know which algorithm was used for embedding. In terms of practical use, the most important are “blind steganalysis” methods that can be applied to image files because we do not have the original cover work for comparison. This philosophiæ doctor thesis describes the methodology to the issues of image steganalysis.In this work, it is crucial to understand the behavior of the targeted steganography algorithm. Then we can use it is weaknesses to increase the detection capability and success of categorization. We are primarily focusing on breaking the steganography algorithm OutGuess2.0. and secondary on breaking the F5 algorithm. We are analyzing the detector's ability, which utilizes a calibration process, blockiness calculation, and shallow neural network, to detect the presence of steganography message in the suspected image. The new approach and results are discussed in this Ph.D. thesis.Steganografie může být využita k nelegálním aktivitám. Proto je velmi důležité být připraven. K detekci steganografického obrázku máme k dispozici techniku známou jako stegoanalýza. Existují různé typy stegoanalýzy v závislosti na tom, zda je znám originální nosič nebo zdali víme, jaký byl použit algoritmus pro vložení tajné zprávy. Z hlediska praktického použití jsou nejdůležitější metody "slepé stagoanalýzy", které zle aplikovat na obrazové soubory a jelikož nemáme originální nosič pro srovnání. Tato doktorská práce popisuje metodologii obrazové stegoanalýzy. V této práci je důležité porozumět chování cíleného steganografického algoritmu. Pak můžeme využít jeho slabiny ke zvýšení detekční schopnosti a úspěšnosti kategorizace. Primárně se zaměřujeme na prolomení steganografického algoritmu OutGuess2.0 a sekundárně na algoritmus F5. Analyzujeme schopnost detektoru, který využívá proces kalibrace, výpočtu shlukování a mělkou neuronovou síť k detekci přítomnosti steganografické zprávy na podezřelém snímku. Nový přístup a výsledky jsou sepsány v této doktorské práci.460 - Katedra informatikyvyhově

Enhanced Multimedia Exchanges over the Internet

Although the Internet was not originally designed for exchanging multimedia streams, consumers heavily depend on it for audiovisual data delivery. The intermittent nature of multimedia traffic, the unguaranteed underlying communication infrastructure, and dynamic user behavior collectively result in the degradation of Quality-of-Service (QoS) and Quality-of-Experience (QoE) perceived by end-users. Consequently, the volume of signalling messages is inevitably increased to compensate for the degradation of the desired service qualities. Improved multimedia services could leverage adaptive streaming as well as blockchain-based solutions to enhance media-rich experiences over the Internet at the cost of increased signalling volume. Many recent studies in the literature provide signalling reduction and blockchain-based methods for authenticated media access over the Internet while utilizing resources quasi-efficiently. To further increase the efficiency of multimedia communications, novel signalling overhead and content access latency reduction solutions are investigated in this dissertation including: (1) the first two research topics utilize steganography to reduce signalling bandwidth utilization while increasing the capacity of the multimedia network; and (2) the third research topic utilizes multimedia content access request management schemes to guarantee throughput values for servicing users, end-devices, and the network. Signalling of multimedia streaming is generated at every layer of the communication protocol stack; At the highest layer, segment requests are generated, and at the lower layers, byte tracking messages are exchanged. Through leveraging steganography, essential signalling information is encoded within multimedia payloads to reduce the amount of resources consumed by non-payload data. The first steganographic solution hides signalling messages within multimedia payloads, thereby freeing intermediate node buffers from queuing non-payload packets. Consequently, source nodes are capable of delivering control information to receiving nodes at no additional network overhead. A utility function is designed to minimize the volume of overhead exchanged while minimizing visual artifacts. Therefore, the proposed scheme is designed to leverage the fidelity of the multimedia stream to reduce the largest amount of control overhead with the lowest negative visual impact. The second steganographic solution enables protocol translation through embedding packet header information within payload data to alternatively utilize lightweight headers. The protocol translator leverages a proposed utility function to enable the maximum number of translations while maintaining QoS and QoE requirements in terms of packet throughput and playback bit-rate. As the number of multimedia users and sources increases, decentralized content access and management over a blockchain-based system is inevitable. Blockchain technologies suffer from large processing latencies; consequently reducing the throughput of a multimedia network. Reducing blockchain-based access latencies is therefore essential to maintaining a decentralized scalable model with seamless functionality and efficient utilization of resources. Adapting blockchains to feeless applications will then port the utility of ledger-based networks to audiovisual applications in a faultless manner. The proposed transaction processing scheme will enable ledger maintainers in sustaining desired throughputs necessary for delivering expected QoS and QoE values for decentralized audiovisual platforms. A block slicing algorithm is designed to ensure that the ledger maintenance strategy is benefiting the operations of the blockchain-based multimedia network. Using the proposed algorithm, the throughput and latency of operations within the multimedia network are then maintained at a desired level

Robust steganographic techniques for secure biometric-based remote authentication

Biometrics are widely accepted as the most reliable proof of identity, entitlement to services, and for crime-related forensics. Using biometrics for remote authentication is becoming an essential requirement for the development of knowledge-based economy in the digital age. Ensuring security and integrity of the biometric data or templates is critical to the success of deployment especially because once the data compromised the whole authentication system is compromised with serious consequences for identity theft, fraud as well as loss of privacy. Protecting biometric data whether stored in databases or transmitted over an open network channel is a serious challenge and cryptography may not be the answer. The main premise of this thesis is that Digital Steganography can provide an alternative security solutions that can be exploited to deal with the biometric transmission problem. The main objective of the thesis is to design, develop and test steganographic tools to support remote biometric authentication. We focus on investigating the selection of biometrics feature representations suitable for hiding in natural cover images and designing steganography systems that are specific for hiding such biometric data rather than being suitable for general purpose. The embedding schemes are expected to have high security characteristics resistant to several types of steganalysis tools and maintain accuracy of recognition post embedding. We shall limit our investigations to embedding face biometrics, but the same challenges and approaches should help in developing similar embedding schemes for other biometrics. To achieve this our investigations and proposals are done in different directions which explain in the rest of this section. Reviewing the literature on the state-of-art in steganography has revealed a rich source of theoretical work and creative approaches that have helped generate a variety of embedding schemes as well as steganalysis tools but almost all focused on embedding random looking secrets. The review greatly helped in identifying the main challenges in the field and the main criteria for success in terms of difficult to reconcile requirements on embedding capacity, efficiency of embedding, robustness against steganalysis attacks, and stego image quality. On the biometrics front the review revealed another rich source of different face biometric feature vectors. The review helped shaping our primary objectives as (1) identifying a binarised face feature factor with high discriminating power that is susceptible to embedding in images, (2) develop a special purpose content-based steganography schemes that can benefit from the well-defined structure of the face biometric data in the embedding procedure while preserving accuracy without leaking information about the source biometric data, and (3) conduct sufficient sets of experiments to test the performance of the developed schemes, highlight the advantages as well as limitations, if any, of the developed system with regards to the above mentioned criteria. We argue that the well-known LBP histogram face biometric scheme satisfies the desired properties and we demonstrate that our new more efficient wavelet based versions called LBPH patterns is much more compact and has improved accuracy. In fact the wavelet version schemes reduce the number of features by 22% to 72% of the original version of LBP scheme guaranteeing better invisibility post embedding. We shall then develop 2 steganographic schemes. The first is the LSB-witness is a general purpose scheme that avoids changing the LSB-plane guaranteeing robustness against targeted steganalysis tools, but establish the viability of using steganography for remote biometric-based recognition. However, it may modify the 2nd LSB of cover pixels as a witness for the presence of the secret bits in the 1st LSB and thereby has some disadvantages with regards to the stego image quality. Our search for a new scheme that exploits the structure of the secret face LBPH patterns for improved stego image quality has led to the development of the first content-based steganography scheme. Embedding is guided by searching for similarities between the LBPH patterns and the structure of the cover image LSB bit-planes partitioned into 8-bit or 4-bit patterns. We shall demonstrate the excellent benefits of using content-based embedding scheme in terms of improved stego image quality, greatly reduced payload, reduced lower bound on optimal embedding efficiency, robustness against all targeted steganalysis tools. Unfortunately our scheme was not robust against the blind or universal SRM steganalysis tool. However we demonstrated robustness against SRM at low payload when our scheme was modified by restricting embedding to edge and textured pixels. The low payload in this case is sufficient to embed a secret full face LBPH patterns. Our work opens new exciting opportunities to build successful real applications of content-based steganography and presents plenty of research challenges