VERY-RL: An Adaptive Image Steganography Algorithm Based on Reinforcement Learning

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

Steganography is a method of hiding sensitive information within an ordinary file to avoid detection. When the file is received, the hidden information is extracted from the original file. Various types of files can be used to conceal the information, including images, audio files, video files, and text files. This research paper focuses on the use of image files as the medium. To create a stego-image, a secret image is embedded into a cover image. Both the cover and secret images are subjected to a Daubechies 3 Discrete Wavelet Transformation (DWT), then encrypted using Arnold's Cat Map algorithm and fused using a simple alpha blending technique with the cover image. DWT enhances the invisibility of hidden information, making it more challenging to detect by visual inspection or statistical analysis. Arnold’s cat map adds an extra layer of security, making it more difficult for an attacker to extract the message. Alpha blending helps to maintain the visual quality and integrity of the cover image, reducing the chances of noticeable artifacts or anomalies that may arise due to the embedding process. The resulting stego-image appears visually like the original cover image, making it less suspicious to potential attackers. Experimental results have been made on multiple area such as the best value of alpha, which DWT family to use, and different combinations of images as cover and payload images. The results demonstrate that our proposed hybrid model achieves a high level of security, robustness, and imperceptibility in image steganography. Moreover, it outperforms existing methods in terms of image quality metrics, such as achieving a Peak Signal-to-Noise Ratio (PSNR) value of up to 70.8 and a Structural Similarity Index (SSIM) value of up to 0.98982. These metrics indicate that the stego-image retains a remarkable level of visual fidelity and similarity to the cover image, thereby enhancing the overall effectiveness of the steganography technique.

Technologies of virtual and augmented reality are gaining popularity. Virtual reality is used in many areas, including beginning to be used in the field of information and computer security. In addition, virtual reality interfaces are also susceptible to attacks. However, there are still few works on research in this area. This article provides an overview of existing solutions to computer security problems using virtual and augmented reality interfaces, as well as an overview and classification of the identified threats for these interfaces themselves.

Advanced encryption and decryption strategies are of great significance for information protection and data security. Visual optical information encryption and decryption technology plays an important role in the field of information security. However, the current optical information encryption technologies have shortcomings such as the need for external decryption equipment, the inability to read out repeatedly, and information leakage, which hinder their practical application. By combining the excellent thermal response characteristics of the MXene-isocyanate propyl triethoxy silane (IPTS)/polyethylene (PE) bilayer and the structural color generated from the laser fabricated biomimetic structural color surface, an approach of encrypt, decrypt, and transmit information has been proposed. The microgroove-induced structural color is attached to the MXene-IPTS/PE bilayer to form a colored soft actuator (CSA) to realize information encryption and decryption, and information transmission. Benefiting from the unique photon-thermal response of the bilayer actuator and the precise spectrum response of the microgroove-induced structural color, the information encryption and decryption system has the advantages of being simple and reliable, which has the potential application in the field of optical information security.

An urgent problem in the field of information security – the provision of human resources in the field of information security in the Russian Federation is presented. One of the factors influencing this is the lack of the possibility of determining at the initial stage the optimal choice of the future direction of activity of graduates of higher educational institutions with specialization in this field. To solve this problem, it is proposed to use methods of data mining, machine learning and algorithms based on artificial neural networks. For the successful application of data mining methods, several factors should be taken into account: the data set must be large enough. Secondly; the data must be structured and analyzed to extract the most important information; the data analysis methods must be adequate and accurate in order to determine the purpose of the graduate with the highest accuracy. Taking into account these factors, the article suggests a way to form the initial data set and prepare it using statistical methods to predict the success of the professional activity of an information security specialist. The results of this work can be useful for higher educational institutions, specialists in the field of information security, heads of personnel departments who are interested in optimizing the process of selecting qualified employees in this field.

Image steganography is one attempt toward the conversion of communication between sender and receiver. Most of the steganographic methods and algorithms belong to either spatial or transform domain. Most of the time, secret data embedded in stego image gets lost when stego image gets compressed. This secret data loss problem mainly occurs when stego image gets compressed with a lossy compression scheme with a higher compression ratio. In this research review paper, we have presented a survey of image steganography with focus is in the direction on Digital Image Steganography Which Resists against Compression with parallel consideration of payload, imperceptibility, compression ratio, and performance parameters, which represent research work of many researchers in this area and direction. The paper also includes findings derived from a survey of image steganography-related research work done by many researchers. This paper also gives the conclusion and future scope for further research in digital image steganography.

We note that the effective and active struggle of European countries with information terrorism takes place within the framework of the European Union, all its member-countries, as well as neighboring countries and applicants for accession. Therefore, it should be stressed that the EU is pursuing itself a very active policy in the field of information security. At the moment, it brings together highly developed countries that have a tremendous impact on international relations by establishing norms and standards of conduct of states in the political, economic, social, informational and other spheres. The main objective of the EU is to strengthen the European Commission’s dialogue with international organizations and partners on the issue of network security and, in particular, on the growing dependence on electronic networks. Political priorities in the field of information security, defined by the governing bodies of the European Union, are being implemented at the national level by both state authorities and nongovernmental organizations. Analyzing all the above, it can be concluded that within the framework of the EU, information security is considered, first of all, as a state of information networks and systems that provides an adequate level of protection of the integrity, availability, authenticity and confidentiality of information and the appropriate level of counteraction to external negative influences. Priorities of EU policy in the field of information security are the creation and implementation of programs and various technical means of protection of information and communication technologies; development of normative legal acts that establish a list of crimes in the IT sphere and criminal liability; ensuring a high level of public awareness of the risks, threats and ways of protecting their information systems / networks from undesirable effects.

In this article, we focus on a new concept called multichannel steganography, in which a sender is able to transmit different secret data to multiple receivers via the same cover image. First, we propose a general framework to achieve multichannel steganography based on modern steganographic paradigm. Then, we design two schemes to, respectively, achieve separable and sequential multichannel steganography with the proposed framework. In the separable scheme, a receiver can extract secret data for him/her using the corresponding data hiding key. For other parts of secret data, the receiver cannot affirm the existence and, therefore, cannot extract them. In the sequential scheme, the multiple receivers are dependent. The receiver with the highest priority can extract data first. After that, the receiver with the second priority can extract data when authorized by the highest priority receiver, and so on. In addition, the two schemes achieve multichannel steganography without decreasing the undetectability of steganography.

Reversible data hiding (RDH) is an active research area in the field of information security. The RDH scheme allows the transmission of a secret message by embedding it into a cover image, and the receiver can recover the original cover image along with the extraction of the secret message. In this paper, we propose a bit plane compression based RDH scheme to hide a sequence of secret message bits into a grayscale image. In the proposed method, a selected bit plane of the cover image will be compressed using run-length encoding (RLE) scheme. Further, the RLE sequence has been efficiently encoded as a binary sequence using Elias gamma encoding method. The Elias gamma encoded bit sequence concatenated with the secret message bits are used to replace the selected bit plane after performing a sequence of Arnold transform. The Arnold transform helps to find a new scrambled version of the bit plane which is very close to the original bit plane to ensure the visual quality of the stego image. The RLE is a lossless compression technique, therefore recovery of the original image is possible by the receiver. The experimental study of the proposed scheme on the images from standard image dataset (USC-SIPI image dataset) shows that the proposed scheme outperforms the existing scheme in terms of the visual quality of the stego image without compromising the data embedding rate.

Learning ability is the basic characteristic of human intelligence. The July 1, 2005 issue of Science published a list of 125 important questions in science. Among them, the question 94 “What are the limits of learning by machines?”. The annotation “Computers can already beat the world’s best chess players, and they have a wealth of information on the Web to draw on. But abstract reasoning is still beyond any machine”. In recent artificial intelligence has made great progresses. In 1997, the rise of the man-machine war, IBM Supercomputer Deep Blue defeated the chess master Garry Kasparov. On February 14, 2011, IBM’s Watson supercomputer won a practice round against Jeopardy champions Ken Jennings and Brad Rutter. In March 2016, Google DeepMind’s AlphaGo sealed a 4-1 victory over a South Korean Go grandmaster Lee Se-dol. This paper focuses on the machine learning methods of AlphaGo, including reinforcement learning, deep learning, deep reinforcement learning, analysis of the existing problems and the latest research progress. Deep reinforcement learning is the combination of deep learning and reinforcement learning, which can realize the learning algorithm from the perception to action. Simply said, this is the same as human behavior, input sensing information such as vision, and then, direct output action through the deep neural network. Deep reinforcement learning has the potential to learn a variety of skills for the robot to achieve full autonomy. Even though reinforcement learning is practiced successfully, but feature states need to manually set, for complex scene is a difficult thing, especially easy to cause the dimension disaster, and expression is not good. In 2010, Sascha Lange and Martin Riedmiller proposed deep auto-encoder neural networks in reinforcement learning to extract feature, which is used to control the visual correlation. In 2013, DeepMind proposed deep Q-network (DQN) in NIPS 2013, using convolution neural network to extract features, and then applied in reinforcement learning. They continue to improve and published an improved version of DQN on Nature in 2015, which has aroused widespread concern. In order to break through the limits of learning by machines, cognitive machine learning is proposed, which is the combination of machine learning and brain cognition, so that the machine intelligence is constantly evolving, and gradually reaches the human level of artificial intelligence. A cognitive model entitled Consciousness And Memory (CAM) is proposed by author, which consists of memory, consciousness, high-level cognitive functions, perception and motor. High-level cognitive functions of the brain include learning, language, thinking, decision making, emotion, and so on. Learning is a course to accept the stimulus through the nervous system and obtain new behavior, habits and accumulation experience. According to the current research progress of brain science and cognitive science, cognitive machine learning may be interested in learning emergence, procedural memory knowledge learning, learning evolution and so on. For intelligence, so-called evolution is refers to the learning of learning and the structure also follows the change. It is important to record the learning result by structure changing and improve the learning method.

Generative image steganography is a key technology for secure information transmission, but existing deep learning-based generative steganographic methods suffer from an extremely high bit error rate (BER) and degraded steganographic image quality in low-bit-rate embedding tasks in which secret information needs duplication or padding to match the model input size. In addition, it is difficult to balance BER reduction and imperceptibility of stego-images. To address these issues, this paper proposes a novel generative image steganography algorithm based on flip watermarking, with the core novelty of designing a mirror flipping preprocessing mechanism to achieve a redundant watermark and eliminate information errors caused by duplication or padding, and constructing an end-to-end Mini-Hide steganographic framework to integrate flip watermarking with generative steganography for the first time. Specifically, the proposed method first converts the binary bitstream of secret information into a square matrix, and performs vertical, horizontal and vertical–horizontal mirror flipping on the matrix to form a redundant basic watermark, which is then expanded to a secret image with the same size as the cover image. After that, the secret image is preprocessed by a preparation network and then input into an encoding network together with the cover image to generate a stego-image. Finally, the generated stego-image is input into the decoding network to extract the secret image. Subsequently, the inverse operation of flip watermarking is performed on the extracted secret image to recover the original binary bitstream. Extensive experiments are conducted on the public COCO dataset (256×256 pixels) with BER, PSNR, and SSIM, and the proposed method is compared with state-of-the-art generative steganographic methods. Quantitative results show that the proposed method achieves a 0% BER for secret information of 8×8 to 64×64 bits, and the BER is only 0.00002% for 256×256-bit secret information; the PSNR of stego-images reaches 37.75 dB, and the SSIM hits 0.96, which are 7.07 dB and 0.02 higher than those of the classic HiDDeN method (64×64 bit) respectively. We also validated the flip watermark module by integrating into other methods; the results also show that the PSNR of FNNS-D is improved by 13.12 dB (256×256), and the BER of SteganoGAN is reduced by 99.99% (256×256 bit). In addition, the proposed method breaks the embedding size limit of HiDDeN (≤64×64 bit) and supports up to 256×256-bit secret information embedding with stable performance. This work significantly reduces the BER of generative image steganography while improving the visual quality of stego-images, provides a new preprocessing and optimization scheme for low-BER generative steganographic algorithm design, and also offers a universal lightweight module for performance improvement of existing steganographic methods, which has important theoretical and practical significance for enhancing the security and reliability of covert information transmission in the field of information security.

Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate stock and foreign exchange markets due to the advantage of artificial intelligence. Recent years have seen a proliferation of the deep reinforcement learning algorithm’s application in algorithmic trading. DRL agents, which combine price prediction and trading signal production, have been used to construct several completely automated trading systems or strategies. Our objective is to enable interested researchers to stay current and easily imitate earlier findings. In this paper, we have worked to explain the utility of Machine Learning, Deep Learning, Reinforcement Learning, and Deep Reinforcement Learning in Quantitative Finance (QF) and the Stock Market. We also outline potential future study paths in this area based on the overview that was presented before.

In recent years, the recommendation system and robot learning are undoubtedly the two most popular application fields, and the core algorithms supporting these two fields are deep learning based on perception and reinforcement learning based on exploration learning, respectively. How to combine these two fields to better improve the development of the whole machine learning field is the dream of numerous researchers. The Deep Reinforcement Network (DRN) model successfully embedded reinforcement learning into the recommendation system, which provided a good idea for subsequent researchers. However, the disadvantage is also obvious, that is, the DRN model is built for news recommendations, meaning that the DRN model is not transferable, which is also the defect of many current recommendation system models. Meanwhile, the agent learning method adopted by the DRN model is primitive and inefficient. Among many models and algorithms that have emerged in recent years, we use the newly proposed deployment efficiency to measure their comprehensive quality and found that few models focus on both efficiency and performance improvement. To fill the gap of model deployment efficiency neglected by many researchers and to create a model of reinforcement learning agents with stronger performance, we have been exploring and trying to complete research on the Gate Attentional Factorization Machines (GAFM) model. Finally, we successfully integrated the GAFM model and reinforcement learning. The Deep Reinforcement Factorization Machines (DRFM) model proposed in this paper is based on the combination of deep learning with high perception ability and reinforcement learning with high exploration ability, centered on improving the deployment efficiency and learning performance of the model. The GAFM model is modified and upgraded using multidisciplinary techniques, and a new model-based random exploration strategy is proposed to update and optimize the recommendation list efficiently. Through parallel contrast experiments on various datasets, it is proved that the DRFM model surpasses the traditional recommendation system model in all aspects. The DRFM model is far superior to other models in terms of performance and robustness, and also significantly improved in terms of deployment efficiency. At the same time, we conduct a comparative analysis with the latest deep reinforcement learning algorithm and prove the unique advantages of the DRFM model.

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

In recent years, deep reinforcement learning has combined the advantages of reinforcement learning and deep learning, and has made great progress in decision-making tasks. However, the training of deep reinforcement learning requires frequent interactions between the agent and the environment and repeated experiments. Adversaries have chances to poison the sample data collected by the agent by attacking the experimental environment in the training process, thereby bringing security risks and serious consequences to the training process of reinforcement learning. This work is committed to addressing the security risks in the field of deep reinforcement learning. However, this work improves the algorithm from the perspective of sample data filtering, and improves the security performance of deep reinforcement learning algorithm. There are two contributions in this work: one is to defend against adversarial attacks against deep reinforcement learning through cluster analysis and sample value evaluation; the other is to propose a deep reinforcement learning algorithm based on sample value evaluation on the basis of deterministic strategy gradient algorithm. The algorithm uses the clustering method to classify the sample pool, and measures the contribution value and security risk of the sample to the model training through the sample value evaluation. The classic game experiments show that the proposed algorithm is safe and effective. It reduces the threat of the agent falling into the adversarial sample attack and improves the training performance of deep reinforcement learning.