جستجوی مقالات مرتبط با کلیدواژه « نهان کاوی » در نشریات گروه « برق »

Information security is a critical aspect of Electronic Warfare today and is a major focus for many researchers. Steganography, a secure communication method, involves concealing information within other data or content, with audio data offering a higher capacity for hiding information. This article introduces a novel approach for detecting steganography in three audio encoders: LPC, CELP, and MELP, known for their effectiveness in audio encoders. The Steganography technique discussed here involves embedding data in the least significant bit of the audio media. Audio analysis is carried out using the RUNS test features. Variations are identified and utilized to train a neural network (LVQ) by comparing these features in the cover and Stego audio files. The neural network is then employed for classification, and the proposed algorithm is tested on the audio samples. The proposed classification method demonstrates an average accuracy of 93.59% in detecting steganography, showcasing its effectiveness compared to other existing methods.

Steganography is the art of transferring information through secret communication. The essential aim of steganography is to minimize the distortion caused by embedding the secret message; so that the image containing the message (stego) cannot be distinguished from the original image (cover), and the existence of the hidden message cannot be detected. The distortion in content-adaptive steganography depends on the local structure of the image. The embedding changes into the areas with rich textures are less detectable than smooth areas, so the textured areas have a higher modification priority. In this regard, three main steganography approaches are proposed: model-based, cost-based, and adversarial. The model-based approach considers a statistical model for the cover image and tries to preserve this model during the embedding process. The cost-based one focuses on minimizing the distortion obtained from the sum of the heuristic costs of modified pixels. The adversarial approach uses the competition between steganography and steganalysis to improve the embedding performance. In the first section of this paper, the concept of steganography and its history is expressed. Digital steganography including three types of cover synthesis, selection, and modification is introduced in the second section. The focus of this paper is on steganography based on the cover modification. The goal is to estimate the best probability distribution of modifications, and embedding the message in the estimated places is left to existing coding algorithms. In the third section, the problem of estimating the probability distribution is formulated as an optimization problem with the aim of distortion minimization. The distortion-based methods compute the probability distribution of embedding changes using a pre-defined distortion function. In the additive distortion function, the embedding changes are assumed to be independent. Thus, the distortion function cannot capture interactions between changes caused by embedding, and it leads the performance to suboptimality. In this regard, the non-additive distortion functions are presented that consider the dependencies among the modification of adjacent pixels. The distortion-based methods include two model-based and cost-based approaches are introduced in the fourth section. Then, their most significant methods are reviewed in the fifth section. Considering the competitive nature of steganography and steganalysis, a new steganography approach is presented in the sixth section that takes advantage of adversarial learning to improve secrecy. Adversarial learning includes two strategies: Generative adversarial networks (GANs) and adversarial attacks. In the concept of steganography, the GAN-based strategy tries to train the steganographic network against a steganalysis network. This is an iterative and dynamic game between steganographic and steganalysis networks to reach the Nash equilibrium. Another strategy attempts to simulate an adversarial attack and generate stego images that deceive the steganalysis network. The adversarial-based steganography methods are reviewed in the seventh section. In the eighth section, different methods are compared from various points of view. The results of this study show that some techniques, such as smoothing the embedding changes, considering the interactions between the changes, using side-informed information, and exploring adversarial networks, can help to estimate the proper embedding probability map and improve performance and security. In the ninth section, suggestions are stated that can be considered for future research. Finally, the conclusion is expressed in the tenth section.

Dramatic changes in digital communication and exchange of image, audio, video and text files result in a suitable field for interpersonal transfers of hidden information. Therefore, nowadays, preserving channel security and intellectual property and access to hidden information make new fields of researches naming steganography, watermarking and steganalysis. Steganalysis as a binary classification distinguish clean signals from stego signals. Features extracted from time and transform domain are proper for this classifier.
Some of steganalysis methods are depended on a specific steganography algorithm and others are independent. The second group of methods are called Universal steganalysis. Universal steganalysis methods are widely used in applications because of their independency to steganography algorithms. These algorithms are based on characteristics such as distortion measurements, higher order statistics and other similar features.
In this research we try to achieve more reliable and accurate results using analytical review of features, choose more effective of them and optimize SVM performance.
In new researches Mel Frequency Cepstral Coefficient and Markov transition probability matrix coefficients are used to steganalysis design. In this paper we consider two facts. First, MFCC extract signal features in transform domain similar to human hearing model, which is more sensitive to low frequency signals. As a result, in this method there is more hidden information mostly in higher frequency audio signals. Therefore, it is suggested to use reversed MFCC. Second, there is an interframe correlation in audio signals which is useful as an information hiding effect.
For the first time, in this research, this features is used in steganalysis field. To have more accurate and stable results, we use recursive feature elimination with correlation bias reduction for SVM.
To implement suggested algorithm, we use two different data sets from TIMIT and GRID. For each data sets,Steghide and LSB-Matching steganography methods implement with 20 and 50 percent capacity. In addition, one of the LIBSVM 3.2 toolboxes is sued for implementation.
Finally, the results show accuracy of steganalysis, four to six percent increase in comparison with previous methods. The ROC of methods clearly shows this improvement.

JPEG is the most applicable image format in digital communication. In recent years, various steganography methods have been proposed for it. This paper aims to study and classify JPEG steganography schemes and introduce different methods to improve their security based on cover. Accordingly, the effective factors in security that are related to the cover such as double compression, spatial frequencies and quality factor have been evaluated theoretically and experimentally. Also some well-known steganography algorithms and software tools have been introduced, evaluated and classified based on different criteria. Some of these algorithms have been implemented in Stegotest software and the destruction effects of steganography in different methods have been compared.