Image Steganography: A Review of the Recent Advances

Image Steganography is the process of hiding information which can be text, image or video inside a cover image. The secret information is hidden in a way that it not visible to the human eyes. Deep learning technology, which has emerged as a powerful tool in various applications including image steganography, has received increased attention recently. The main goal of this paper is to explore and discuss various deep learning methods available in image steganography field. Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field

End-to-end image steganography using deep convolutional autoencoders

Image steganography is used to hide a secret image inside a cover image in plain sight. Traditionally, the secret data is converted into binary bits and the cover image is manipulated statistically to embed the secret binary bits. Overloading the cover image may lead to distortions and the secret information may become visible. Hence the hiding capacity of the traditional methods are limited. In this paper, a light-weight yet simple deep convolutional autoencoder architecture is proposed to embed a secret image inside a cover image as well as to extract the embedded secret image from the stego image. The proposed method is evaluated using three datasets - COCO, CelebA and ImageNet. Peak Signal-to-Noise Ratio, hiding capacity and imperceptibility results on the test set are used to measure the performance. The proposed method has been evaluated using various images including Lena, airplane, baboon and peppers and compared against other traditional image steganography methods. The experimental results have demonstrated that the proposed method has higher hiding capacity, security and robustness, and imperceptibility performances than other deep learning image steganography methods

Security and Privacy for the Modern World

The world is organized around technology that does not respect its users. As a precondition of participation in digital life, users cede control of their data to third-parties with murky motivations, and cannot ensure this control is not mishandled or abused. In this work, we create secure, privacy-respecting computing for the average user by giving them the tools to guarantee their data is shielded from prying eyes. We first uncover the side channels present when outsourcing scientific computation to the cloud, and address them by building a data-oblivious virtual environment capable of efficiently handling these workloads. Then, we explore stronger privacy protections for interpersonal communication through practical steganography, using it to hide sensitive messages in realistic cover distributions like English text. Finally, we discuss at-home cryptography, and leverage it to bind a user’s access to their online services and important files to a secure location, such as their smart home. This line of research represents a new model of digital life, one that is both full-featured and protected against the security and privacy threats of the modern world

반향 환경에 강인한 음향 데이터 전송을 위한 오디오 정보 은닉 기법 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 김남수.In this dissertation, audio data hiding methods suitable for acoustic data transmission are studied. Acoustic data transmission implies a technique which communicates data in short-range aerial space between a loudspeaker and a microphone. Audio data hiding method implies a technique that embeds message signals into audio such as music or speech. The audio signal with embedded message is played back by the loudspeaker at a transmitter and the signal is recorded by the microphone at a receiver without any additional communication devices. The data hiding methods for acoustic data transmission require a high level of robustness and data rate than those for other applications. For one of the conventional methods, the acoustic orthogonal frequency division multiplexing (AOFDM) technique was developed as a reliable communication with reasonable bit rate. The conventional methods including AOFDM, however, are considered deficient in transmission performance or audio quality. To overcome this limitation, the modulated complex lapped transform (MCLT) is introduced in the second chapter of the dissertation. The system using MCLT does not produce blocking artifacts which may degrade the quality of the resulting data-embedded audio signal. Moreover, the interference among adjacent coefficients due to the overlap property is analyzed to take advantage of it for data embedding and extraction. In the third chapter of the dissertation, a novel audio data hiding method for the acoustic data transmission using MCLT is proposed. In the proposed system, audio signal is transformed by the MCLT and the phases of the coefficients are modified to embed message based on the fact that human auditory perception is more sensitive to the variation in magnitude spectra. In the proposed method, the perceived quality of the data-embedded audio signal can be kept almost similar to that of the original audio while transmitting data at several hundreds of bits per second (bps). The experimental results have shown that the audio quality and transmission performance of proposed system are better than those of the AOFDM based system. Moreover, several techniques have been found to further improve the performance of the proposed acoustic data transmission system which are listed as follows: incorporating a masking threshold (MM), clustering based decoding (CLS), and a spectral magnitude adjustment (SMA). In the fourth chapter of the dissertation, an audio data hiding technique more suitable for acoustic data transmission in reverberant environments is proposed. In this approach, sophisticated techniques widely deployed in wireless communication is incorporated which can be summarized as follows: First, a proper range of MCLT length to cope with reverberant environments is analyzed based on the wireless communication theory. Second, a channel estimation technique based on the Wiener estimator to compensate the effect of channel is applied in conjunction with a suitable data packet structure. From the experimental result, the MCLT length longer than the reverberation time is found to be robust against the reverberant environments at the cost of the quality of the data-embedded audio. The experimental results have also shown that the proposed method is robust against various forms of attacks such as signal processing, overwriting, and malicious removal methods. However, it would be the most severe problem to find a proper window length which satisfies both the inaudible distortion and robust data transmission in the reverberant environments. For the phase modification of the audio signal, it would be highly likely to incur a significant quality degradation if the length of time-frequency transform is very long due to the pre-echo phenomena. In the fifth chapter, therefore, segmental SNR adjustment (SSA) technique is proposed to further modify the spectral components for attenuating the pre-echo. In the proposed SSA technique, segmenatal SNR is calculated from short-length MCLT analysis and its minimum value is limited to a desired value. The experimental results have shown that the SSA algorithm with a long MCLT length can attenuate the pre-echo effectively such that it can transmit data more reliably while preserving good audio quality. In addition, a good trade-off between the audio quality and transmission performance can be achieved by adjusting only a single parameter in the SSA algorithm. If the number of microphones is more than one, the diversity technique which takes advantage of transmitting duplicates through statistically independent channel could be useful to enhance the transmission reliability. In the sixth chapter, the acoustic data transmission technique is extended to take advantage of the multi-microphone scheme based on combining. In the combining-based multichannel method, the synchronization and channel estimation are respectively performed at each received signal and then the received signals are linearly combined so that the SNR is increased. The most noticeable property for combining-based technique is to provide compatibility with the acoustic data transmission system using a single microphone. From the series of the experiments, the proposed multichannel method have been found to be useful to enhance the transmission performance despite of the statistical dependency between the channels.Abstract i List of Figures ix List of Tables xv Chapter 1 Introduction 1 1.1 Audio Data Hiding and Acoustic Data Transmission 1 1.2 Previous Methods 4 1.2.1 Audio Watermarking Based Methods 4 1.2.2 Wireless Communication Based Methods 6 1.3 Performance Evaluation 9 1.3.1 Audio Quality 9 1.3.2 Data Transmission Performance 10 1.4 Outline of the Dissertation 10 Chapter 2 Modulated Complex Lapped Transform 13 2.1 Introduction 13 2.2 MCLT 14 2.3 Fast Computation Algorithm 18 2.4 Derivation of Interference Terms in MCLT 19 2.5 Summary 24 Chapter 3 Acoustic Data Transmission Based on MCLT 25 3.1 Introduction 25 3.2 Data Embedding 27 3.2.1 Message Frame 27 3.2.2 Synchronization Frame 29 3.2.3 Data Packet Structure 32 3.3 Data Extraction 32 3.4 Techniques for Performance Enhancement 33 3.4.1 Magnitude Modification Based on Frequency Masking 33 3.4.2 Clustering-based Decoding 35 3.4.3 Spectral Magnitude Adjustment Algorithm 37 3.5 Experimental Results 39 3.5.1 Comparison with Acoustic OFDM 39 3.5.2 Performance Improvements by Magnitude Modification and Clustering based Decoding 47 3.5.3 Performance Improvements by Spectral Magnitude Adjustment 50 3.6 Summary 52 Chapter 4 Robust Acoustic Data Transmission against Reverberant Environments 55 4.1 Introduction 55 4.2 Data Embedding 56 4.2.1 Data Embedding 57 4.2.2 MCLT Length 58 4.2.3 Data Packet Structure 60 4.3 Data Extraction 61 4.3.1 Synchronization 61 4.3.2 Channel Estimation and Compensation 62 4.3.3 Data Decoding 65 4.4 Experimental Results 66 4.4.1 Robustness to Reverberation 69 4.4.2 Audio Quality 71 4.4.3 Robustness to Doppler Effect 71 4.4.4 Robustness to Attacks 71 4.5 Summary 75 Chapter 5 Segmental SNR Adjustment for Audio Quality Enhancement 77 5.1 Introduction 77 5.2 Segmental SNR Adjustment Algorithm 79 5.3 Experimental Results 83 5.3.1 System Configurations 83 5.3.2 Audio Quality Test 84 5.3.3 Robustness to Attacks 86 5.3.4 Transmission Performance of Recorded Signals in Indoor Environment 87 5.3.5 Error correction using convolutional coding 89 5.4 Summary 91 Chapter 6 Multichannel Acoustic Data Transmission 93 6.1 Introduction 93 6.2 Multichannel Techniques for Robust Data Transmission 94 6.2.1 Diversity Techniques for Multichannel System 94 6.2.2 Combining-based Multichannel Acoustic Data Transmission 98 6.3 Experimental Results 100 6.3.1 Room Environments 101 6.3.2 Transmission Performance of Simulated Environments 102 6.3.3 Transmission Performance of Recorded Signals in Reverberant Environment 105 6.4 Summary 106 Chapter 7 Conclusions 109 Bibliography 113 국문초록 121Docto

Authentication and Data Protection under Strong Adversarial Model

We are interested in addressing a series of existing and plausible threats to cybersecurity where the adversary possesses unconventional attack capabilities. Such unconventionality includes, in our exploration but not limited to, crowd-sourcing, physical/juridical coercion, substantial (but bounded) computational resources, malicious insiders, etc. Our studies show that unconventional adversaries can be counteracted with a special anchor of trust and/or a paradigm shift on a case-specific basis. Complementing cryptography, hardware security primitives are the last defense in the face of co-located (physical) and privileged (software) adversaries, hence serving as the special trust anchor. Examples of hardware primitives are architecture-shipped features (e.g., with CPU or chipsets), security chips or tokens, and certain features on peripheral/storage devices. We also propose changes of paradigm in conjunction with hardware primitives, such as containing attacks instead of counteracting, pretended compliance, and immunization instead of detection/prevention. In this thesis, we demonstrate how our philosophy is applied to cope with several exemplary scenarios of unconventional threats, and elaborate on the prototype systems we have implemented. Specifically, Gracewipe is designed for stealthy and verifiable secure deletion of on-disk user secrets under coercion; Hypnoguard protects in-RAM data when a computer is in sleep (ACPI S3) in case of various memory/guessing attacks; Uvauth mitigates large-scale human-assisted guessing attacks by receiving all login attempts in an indistinguishable manner, i.e., correct credentials in a legitimate session and incorrect ones in a plausible fake session; Inuksuk is proposed to protect user files against ransomware or other authorized tampering. It augments the hardware access control on self-encrypting drives with trusted execution to achieve data immunization. We have also extended the Gracewipe scenario to a network-based enterprise environment, aiming to address slightly different threats, e.g., malicious insiders. We believe the high-level methodology of these research topics can contribute to advancing the security research under strong adversarial assumptions, and the promotion of software-hardware orchestration in protecting execution integrity therein

Sensor Data Integrity Verification for Real-time and Resource Constrained Systems

Sensors are used in multiple applications that touch our lives and have become an integral part of modern life. They are used in building intelligent control systems in various industries like healthcare, transportation, consumer electronics, military, etc. Many mission-critical applications require sensor data to be secure and authentic. Sensor data security can be achieved using traditional solutions like cryptography and digital signatures, but these techniques are computationally intensive and cannot be easily applied to resource constrained systems. Low complexity data hiding techniques, on the contrary, are easy to implement and do not need substantial processing power or memory. In this applied research, we use and configure the established low complexity data hiding techniques from the multimedia forensics domain. These techniques are used to secure the sensor data transmissions in resource constrained and real-time environments such as an autonomous vehicle. We identify the areas in an autonomous vehicle that require sensor data integrity and propose suitable water-marking techniques to verify the integrity of the data and evaluate the performance of the proposed method against different attack vectors. In our proposed method, sensor data is embedded with application specific metadata and this process introduces some distortion. We analyze this embedding induced distortion and its impact on the overall sensor data quality to conclude that watermarking techniques, when properly configured, can solve sensor data integrity verification problems in an autonomous vehicle.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/167387/3/Raghavendar Changalvala Final Dissertation.pdfDescription of Raghavendar Changalvala Final Dissertation.pdf : Dissertatio

Cryptography and Its Applications in Information Security

Nowadays, mankind is living in a cyber world. Modern technologies involve fast communication links between potentially billions of devices through complex networks (satellite, mobile phone, Internet, Internet of Things (IoT), etc.). The main concern posed by these entangled complex networks is their protection against passive and active attacks that could compromise public security (sabotage, espionage, cyber-terrorism) and privacy. This Special Issue “Cryptography and Its Applications in Information Security” addresses the range of problems related to the security of information in networks and multimedia communications and to bring together researchers, practitioners, and industrials interested by such questions. It consists of eight peer-reviewed papers, however easily understandable, that cover a range of subjects and applications related security of information

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity