Adaptive spatial image steganography and steganalysis using perceptual modelling and machine learning

Image steganography is a method for communicating secret messages under the cover images. A sender will embed the secret messages into the cover images according to an algorithm, and then the resulting image will be sent to the receiver. The receiver can extract the secret messages with the predefined algorithm. To counter this kind of technique, image steganalysis is proposed to detect the presence of secret messages. After many years of development, current image steganography uses the adaptive algorithm for embedding the secrets, which automatically finds the complex area in the cover source to avoid being noticed. Meanwhile, image steganalysis has also been advanced to universal steganalysis, which does not require the knowledge of the steganographic algorithm. With the development of the computational hardware, i.e., Graphical Processing Units (GPUs), some computational expensive techniques are now available, i.e., Convolutional Neural Networks (CNNs), which bring a large improvement in the detection tasks in image steganalysis. To defend against the attacks, new techniques are also being developed to improve the security of image steganography, these include designing more scientific cost functions, the key in adaptive steganography, and generating stego images from the knowledge of the CNNs. Several contributions are made for both image steganography and steganalysis in this thesis. Firstly, inspired by the Ranking Priority Profile (RPP), a new cost function for adaptive image steganography is proposed, which uses the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in the design. The RPP mainly includes three rules, i.e., the Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, the Spreading rule is followed to smooth the resulting image produced by 2D-SSA with WMF. The proposed algorithm has improved performance over four benchmarking approaches against non-shared selection channel attacks. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. The approach is much faster than other model-based methods. Secondly, for image steganalysis, to tackle more complex datasets that are close to the real scenarios and to push image steganalysis further to real-life applications, an Enhanced Residual Network with self-attention ability, i.e., ERANet, is proposed. By employing a more mathematically sophisticated way to extract more effective features in the images and the global self-Attention technique, the ERANet can further capture the stego signal in the deeper layers, hence it is suitable for the more complex situations in the new datasets. The proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets have demonstrated the effectiveness of the proposed methodology. Lastly, for image steganography, with the knowledge from the CNNs, a novel postcost-optimization algorithm is proposed. Without modifying the original stego image and the original cost function of the steganography, and no need for training a Generative Adversarial Network (GAN), the proposed method mainly uses the gradient maps from a well-trained CNN to represent the cost, where the original cost map of the steganography is adopted to indicate the embedding positions. This method will smooth the gradient maps before adjusting the cost, which solves the boundary problem of the CNNs having multiple subnets. Extensive experiments have been carried out to validate the effectiveness of the proposed method, which provides state-of-the-art performance. In addition, compared to existing work, the proposed method is effcient in computing time as well. In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages. For image steganalysis, a new CNN architecture has also been proposed, which utilizes multiple techniques for providing state of-the-art performance. Future directions are also discussed for indicating potential research.Image steganography is a method for communicating secret messages under the cover images. A sender will embed the secret messages into the cover images according to an algorithm, and then the resulting image will be sent to the receiver. The receiver can extract the secret messages with the predefined algorithm. To counter this kind of technique, image steganalysis is proposed to detect the presence of secret messages. After many years of development, current image steganography uses the adaptive algorithm for embedding the secrets, which automatically finds the complex area in the cover source to avoid being noticed. Meanwhile, image steganalysis has also been advanced to universal steganalysis, which does not require the knowledge of the steganographic algorithm. With the development of the computational hardware, i.e., Graphical Processing Units (GPUs), some computational expensive techniques are now available, i.e., Convolutional Neural Networks (CNNs), which bring a large improvement in the detection tasks in image steganalysis. To defend against the attacks, new techniques are also being developed to improve the security of image steganography, these include designing more scientific cost functions, the key in adaptive steganography, and generating stego images from the knowledge of the CNNs. Several contributions are made for both image steganography and steganalysis in this thesis. Firstly, inspired by the Ranking Priority Profile (RPP), a new cost function for adaptive image steganography is proposed, which uses the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in the design. The RPP mainly includes three rules, i.e., the Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, the Spreading rule is followed to smooth the resulting image produced by 2D-SSA with WMF. The proposed algorithm has improved performance over four benchmarking approaches against non-shared selection channel attacks. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. The approach is much faster than other model-based methods. Secondly, for image steganalysis, to tackle more complex datasets that are close to the real scenarios and to push image steganalysis further to real-life applications, an Enhanced Residual Network with self-attention ability, i.e., ERANet, is proposed. By employing a more mathematically sophisticated way to extract more effective features in the images and the global self-Attention technique, the ERANet can further capture the stego signal in the deeper layers, hence it is suitable for the more complex situations in the new datasets. The proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets have demonstrated the effectiveness of the proposed methodology. Lastly, for image steganography, with the knowledge from the CNNs, a novel postcost-optimization algorithm is proposed. Without modifying the original stego image and the original cost function of the steganography, and no need for training a Generative Adversarial Network (GAN), the proposed method mainly uses the gradient maps from a well-trained CNN to represent the cost, where the original cost map of the steganography is adopted to indicate the embedding positions. This method will smooth the gradient maps before adjusting the cost, which solves the boundary problem of the CNNs having multiple subnets. Extensive experiments have been carried out to validate the effectiveness of the proposed method, which provides state-of-the-art performance. In addition, compared to existing work, the proposed method is effcient in computing time as well. In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages. For image steganalysis, a new CNN architecture has also been proposed, which utilizes multiple techniques for providing state of-the-art performance. Future directions are also discussed for indicating potential research

Image statistical frameworks for digital image forensics

The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet personal computers as well as high-speed networks have made a digital image a conventional medium for visual information. Creation, duplication, distribution, or tampering of such a medium can be easily done, which calls for the necessity to be able to trace back the authenticity or history of the medium. Digital image forensics is an emerging research area that aims to resolve the imposed problem and has grown in popularity over the past decade. On the other hand, anti-forensics has emerged over the past few years as a relatively new branch of research, aiming at revealing the weakness of the forensic technology. These two sides of research move digital image forensic technologies to the next higher level. Three major contributions are presented in this dissertation as follows. First, an effective multi-resolution image statistical framework for digital image forensics of passive-blind nature is presented in the frequency domain. The image statistical framework is generated by applying Markovian rake transform to image luminance component. Markovian rake transform is the applications of Markov process to difference arrays which are derived from the quantized block discrete cosine transform 2-D arrays with multiple block sizes. The efficacy and universality of the framework is then evaluated in two major applications of digital image forensics: 1) digital image tampering detection; 2) classification of computer graphics and photographic images. Second, a simple yet effective anti-forensic scheme is proposed, capable of obfuscating double JPEG compression artifacts, which may vital information for image forensics, for instance, digital image tampering detection. Shrink-and-zoom (SAZ) attack, the proposed scheme, is simply based on image resizing and bilinear interpolation. The effectiveness of SAZ has been evaluated over two promising double JPEG compression schemes and the outcome reveals that the proposed scheme is effective, especially in the cases that the first quality factor is lower than the second quality factor. Third, an advanced textural image statistical framework in the spatial domain is proposed, utilizing local binary pattern (LBP) schemes to model local image statistics on various kinds of residual images including higher-order ones. The proposed framework can be implemented either in single- or multi-resolution setting depending on the nature of application of interest. The efficacy of the proposed framework is evaluated on two forensic applications: 1) steganalysis with emphasis on HUGO (Highly Undetectable Steganography), an advanced steganographic scheme embedding hidden data in a content-adaptive manner locally into some image regions which are difficult for modeling image statics; 2) image recapture detection (IRD). The outcomes of the evaluations suggest that the proposed framework is effective, not only for detecting local changes which is in line with the nature of HUGO, but also for detecting global difference (the nature of IRD)

MODIFIED MULTI-LEVEL STEGANOGRAPHY TO ENHANCE DATA SECURITY

Data-hiding using steganography algorithm becomes an important technique to prevent unauthorized users to have access to a secret data.  In this paper, steganography algorithm has been constructed to hide a secret data in a gray and a color images, this algorithm is named deep hiding/extraction algorithm (DHEA) to modify multi-level steganography (MLS). The suggested hiding algorithm is based on modified least significant bit (MDLSB) to scatter data in a cover-image and it utilizes a number of levels; where each level perform hiding data on a gray image except the last level that applies a color image to keep secret data. Furthermore, proper randomization approach with two layers is implemented; the first layer uses random pixels selection for hiding a secret data at each level, while the second layer implements at the last level to move randomly from segment to the others. In addition, the proposed hiding algorithm implements an effective lossless image compression using DEFLATE algorithm to make it possible to hide data into a next level. Dynamic encryption algorithm based on Advanced Encryption Standard (AES) is applied at each level by changing cipher keys (Ck) from level to the next, this approach has been applied to increase the security and working against attackers. Soft computing using a meta-heuristic approach based on artificial bee colony (ABC) algorithm has been introduced to achieve smoothing on pixels of stego-image, this approach is effective to reduce the noise caused by a hidden large amount of data and to increase a stego-image quality on the last level. The experimental result demonstrates the effectiveness of the proposed algorithm with bee colony DHA-ABC to show high-performing to hide a large amount of data up to four bits per pixel (bpp) with high security in terms of hard extraction of a secret message and noise reduction of the stego-image. Moreover, using deep hiding with unlimited levels is promising to confuse attackers and to compress a deep sequence of images into one image

인공지능 보안

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 협동과정 생물정보학전공, 2021. 2. 윤성로.With the development of machine learning (ML), expectations for artificial intelligence (AI) technologies have increased daily. In particular, deep neural networks have demonstrated outstanding performance in many fields. However, if a deep-learning (DL) model causes mispredictions or misclassifications, it can cause difficulty, owing to malicious external influences. This dissertation discusses DL security and privacy issues and proposes methodologies for security and privacy attacks. First, we reviewed security attacks and defenses from two aspects. Evasion attacks use adversarial examples to disrupt the classification process, and poisoning attacks compromise training by compromising the training data. Next, we reviewed attacks on privacy that can exploit exposed training data and defenses, including differential privacy and encryption. For adversarial DL, we study the problem of finding adversarial examples against ML-based portable document format (PDF) malware classifiers. We believe that our problem is more challenging than those against ML models for image processing, owing to the highly complex data structure of PDFs, compared with traditional image datasets, and the requirement that the infected PDF should exhibit malicious behavior without being detected. We propose an attack using generative adversarial networks that effectively generates evasive PDFs using a variational autoencoder robust against adversarial examples. For privacy in DL, we study the problem of avoiding sensitive data being misused and propose a privacy-preserving framework for deep neural networks. Our methods are based on generative models that preserve the privacy of sensitive data while maintaining a high prediction performance. Finally, we study the security aspect in biological domains to detect maliciousness in deoxyribonucleic acid sequences and watermarks to protect intellectual properties. In summary, the proposed DL models for security and privacy embrace a diversity of research by attempting actual attacks and defenses in various fields.인공지능 모델을 사용하기 위해서는 개인별 데이터 수집이 필수적이다. 반면 개인의 민감한 데이터가 유출되는 경우에는 프라이버시 침해의 소지가 있다. 인공지능 모델을 사용하는데 수집된 데이터가 외부에 유출되지 않도록 하거나, 익명화, 부호화 등의 보안 기법을 인공지능 모델에 적용하는 분야를 Private AI로 분류할 수 있다. 또한 인공지능 모델이 노출될 경우 지적 소유권이 무력화될 수 있는 문제점과, 악의적인 학습 데이터를 이용하여 인공지능 시스템을 오작동할 수 있고 이러한 인공지능 모델 자체에 대한 위협은 Secure AI로 분류할 수 있다. 본 논문에서는 학습 데이터에 대한 공격을 기반으로 신경망의 결손 사례를 보여준다. 기존의 AEs 연구들은 이미지를 기반으로 많은 연구가 진행되었다. 보다 복잡한 heterogenous한 PDF 데이터로 연구를 확장하여 generative 기반의 모델을 제안하여 공격 샘플을 생성하였다. 다음으로 이상 패턴을 보이는 샘플을 검출할 수 있는 DNA steganalysis 방어 모델을 제안한다. 마지막으로 개인 정보 보호를 위해 generative 모델 기반의 익명화 기법들을 제안한다. 요약하면 본 논문은 인공지능 모델을 활용한 공격 및 방어 알고리즘과 신경망을 활용하는데 발생되는 프라이버시 이슈를 해결할 수 있는 기계학습 알고리즘에 기반한 일련의 방법론을 제안한다.Abstract i List of Figures vi List of Tables xiii 1 Introduction 1 2 Background 6 2.1 Deep Learning: a brief overview . . . . . . . . . . . . . . . . . . . 6 2.2 Security Attacks on Deep Learning Models . . . . . . . . . . . . . 10 2.2.1 Evasion Attacks . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.2 Poisoning Attack . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Defense Techniques Against Deep Learning Models . . . . . . . . . 26 2.3.1 Defense Techniques against Evasion Attacks . . . . . . . . 27 2.3.2 Defense against Poisoning Attacks . . . . . . . . . . . . . . 36 2.4 Privacy issues on Deep Learning Models . . . . . . . . . . . . . . . 38 2.4.1 Attacks on Privacy . . . . . . . . . . . . . . . . . . . . . . 39 2.4.2 Defenses Against Attacks on Privacy . . . . . . . . . . . . 40 3 Attacks on Deep Learning Models 47 3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.1.1 Threat Model . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.1.2 Portable Document Format (PDF) . . . . . . . . . . . . . . 55 3.1.3 PDF Malware Classifiers . . . . . . . . . . . . . . . . . . . 57 3.1.4 Evasion Attacks . . . . . . . . . . . . . . . . . . . . . . . 58 3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.2.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . 60 3.2.2 Feature Selection Process . . . . . . . . . . . . . . . . . . 61 3.2.3 Seed Selection for Mutation . . . . . . . . . . . . . . . . . 62 3.2.4 Evading Model . . . . . . . . . . . . . . . . . . . . . . . . 63 3.2.5 Model architecture . . . . . . . . . . . . . . . . . . . . . . 67 3.2.6 PDF Repacking and Verification . . . . . . . . . . . . . . . 67 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.1 Datasets and Model Training . . . . . . . . . . . . . . . . . 68 3.3.2 Target Classifiers . . . . . . . . . . . . . . . . . . . . . . . 71 3.3.3 CVEs for Various Types of PDF Malware . . . . . . . . . . 72 3.3.4 Malicious Signature . . . . . . . . . . . . . . . . . . . . . 72 3.3.5 AntiVirus Engines (VirusTotal) . . . . . . . . . . . . . . . 76 3.3.6 Feature Mutation Result for Contagio . . . . . . . . . . . . 76 3.3.7 Feature Mutation Result for CVEs . . . . . . . . . . . . . . 78 3.3.8 Malicious Signature Verification . . . . . . . . . . . . . . . 78 3.3.9 Evasion Speed . . . . . . . . . . . . . . . . . . . . . . . . 80 3.3.10 AntiVirus Engines (VirusTotal) Result . . . . . . . . . . . . 82 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4 Defense on Deep Learning Models 88 4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.1.1 Message-Hiding Regions . . . . . . . . . . . . . . . . . . . 91 4.1.2 DNA Steganography . . . . . . . . . . . . . . . . . . . . . 92 4.1.3 Example of Message Hiding . . . . . . . . . . . . . . . . . 94 4.1.4 DNA Steganalysis . . . . . . . . . . . . . . . . . . . . . . 95 4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4.2.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.2.2 Proposed Model Architecture . . . . . . . . . . . . . . . . 103 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 4.3.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . 105 4.3.2 Environment . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.3.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.3.4 Model Training . . . . . . . . . . . . . . . . . . . . . . . . 107 4.3.5 Message Hiding Procedure . . . . . . . . . . . . . . . . . . 108 4.3.6 Evaluation Procedure . . . . . . . . . . . . . . . . . . . . . 109 4.3.7 Performance Comparison . . . . . . . . . . . . . . . . . . . 109 4.3.8 Analyzing Malicious Code in DNA Sequences . . . . . . . 112 4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 5 Privacy: Generative Models for Anonymizing Private Data 115 5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 5.1.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 5.1.2 Anonymization using GANs . . . . . . . . . . . . . . . . . 119 5.1.3 Security Principle of Anonymized GANs . . . . . . . . . . 123 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 5.2.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 5.2.2 Target Classifiers . . . . . . . . . . . . . . . . . . . . . . . 126 5.2.3 Model Training . . . . . . . . . . . . . . . . . . . . . . . . 126 5.2.4 Evaluation Process . . . . . . . . . . . . . . . . . . . . . . 126 5.2.5 Comparison to Differential Privacy . . . . . . . . . . . . . 128 5.2.6 Performance Comparison . . . . . . . . . . . . . . . . . . . 128 5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 6 Privacy: Privacy-preserving Inference for Deep Learning Models 132 6.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 6.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 135 6.1.2 Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.1.3 Deep Private Generation Framework . . . . . . . . . . . . . 137 6.1.4 Security Principle . . . . . . . . . . . . . . . . . . . . . . . 141 6.1.5 Threat to the Classifier . . . . . . . . . . . . . . . . . . . . 143 6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6.2.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6.2.2 Experimental Process . . . . . . . . . . . . . . . . . . . . . 146 6.2.3 Target Classifiers . . . . . . . . . . . . . . . . . . . . . . . 147 6.2.4 Model Training . . . . . . . . . . . . . . . . . . . . . . . . 147 6.2.5 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . 149 6.2.6 Performance Comparison . . . . . . . . . . . . . . . . . . . 150 6.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 7 Conclusion 153 7.0.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 154 7.0.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 155 Bibliography 157 Abstract in Korean 195Docto

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Markov bidirectional transfer matrix for detecting LSB speech steganography with low embedding rates

Steganalysis with low embedding rates is still a challenge in the field of information hiding. Speech signals are typically processed by wavelet packet decomposition, which is capable of depicting the details of signals with high accuracy. A steganography detection algorithm based on the Markov bidirectional transition matrix (MBTM) of the wavelet packet coefficient (WPC) of the second-order derivative-based speech signal is proposed. On basis of the MBTM feature, which can better express the correlation of WPC, a Support Vector Machine (SVM) classifier is trained by a large number of Least Significant Bit (LSB) hidden data with embedding rates of 1%, 3%, 5%, 8%,10%, 30%, 50%, and 80%. LSB matching steganalysis of speech signals with low embedding rates is achieved. The experimental results show that the proposed method has obvious superiorities in steganalysis with low embedding rates compared with the classic method using histogram moment features in the frequency domain (HMIFD) of the second-order derivative-based WPC and the second-order derivative-based Mel-frequency cepstral coefficients (MFCC). Especially when the embedding rate is only 3%, the accuracy rate improves by 17.8%, reaching 68.5%, in comparison with the method using HMIFD features of the second derivative WPC. The detection accuracy improves as the embedding rate increases

Multimedia Forensic Analysis via Intrinsic and Extrinsic Fingerprints

Digital imaging has experienced tremendous growth in recent decades, and digital images have been used in a growing number of applications. With such increasing popularity of imaging devices and the availability of low-cost image editing software, the integrity of image content can no longer be taken for granted. A number of forensic and provenance questions often arise, including how an image was generated; from where an image was from; what has been done on the image since its creation, by whom, when and how. This thesis presents two different sets of techniques to address the problem via intrinsic and extrinsic fingerprints. The first part of this thesis introduces a new methodology based on intrinsic fingerprints for forensic analysis of digital images. The proposed method is motivated by the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on the final output data. We present methods to identify these intrinsic fingerprints via component forensic analysis, and demonstrate that these traces can serve as useful features for such forensic applications as to build a robust device identifier and to identify potential technology infringement or licensing. Building upon component forensics, we develop a general authentication and provenance framework to reconstruct the processing history of digital images. We model post-device processing as a manipulation filter and estimate its coefficients using a linear time invariant approximation. Absence of in-device fingerprints, presence of new post-device fingerprints, or any inconsistencies in the estimated fingerprints across different regions of the test image all suggest that the image is not a direct device output and has possibly undergone some kind of processing, such as content tampering or steganographic embedding, after device capture. While component forensics is widely applicable in a number of scenarios, it has performance limitations. To understand the fundamental limits of component forensics, we develop a new theoretical framework based on estimation and pattern classification theories, and define formal notions of forensic identifiability and classifiability of components. We show that the proposed framework provides a solid foundation to study information forensics and helps design optimal input patterns to improve parameter estimation accuracy via semi non-intrusive forensics. The final part of the thesis investigates a complementing extrinsic approach via image hashing that can be used for content-based image authentication and other media security applications. We show that the proposed hashing algorithm is robust to common signal processing operations and present a systematic evaluation of the security of image hash against estimation and forgery attacks

The role of side information in steganography

Das Ziel digitaler Steganographie ist es, eine geheime Kommunikation in digitalen Medien zu verstecken. Der übliche Ansatz ist es, die Nachricht in einem empirischen Trägermedium zu verstecken. In dieser Arbeit definieren wir den Begriff der Steganographischen Seiteninformation (SSI). Diese Definition umfasst alle wichtigen Eigenschaften von SSI. Wir begründen die Definition informationstheoretisch und erklären den Einsatz von SSI. Alle neueren steganographischen Algorithmen nutzen SSI um die Nachricht einzubetten. Wir entwickeln einen Angriff auf adaptive Steganographie und zeigen anhand von weit verbreiteten SSI-Varianten, dass unser Angriff funktioniert. Wir folgern, dass adaptive Steganographie spieltheoretisch beschrieben werden muss. Wir entwickeln ein spieltheoretisches Modell für solch ein System und berechnen die spieltheoretisch optimalen Strategien. Wir schlussfolgern, dass ein Steganograph diesen Strategien folgen sollte. Zudem entwickeln wir eine neue spieltheoretisch optimale Strategie zur Einbettung, die sogenannten Ausgleichseinbettungsstrategien.The  goal of digital steganography is to hide a secret communication in digital media. The common approach in steganography is to hide the secret messages in empirical cover objects. We are the first to define Steganographic Side Information (SSI). Our definition of SSI captures all relevant properties of SSI. We explain the common usage of SSI. All recent steganographic schemes use SSI to identify suitable areas fot the embedding change. We develop a targeted attack on four widely used variants of SSI, and show that our attack detects them almost perfectly. We argue that the steganographic competition must be framed with means of game theory. We present a game-theoretical framework that captures all relevant properties of such a steganographic system. We instantiate the framework with five different models and solve each of these models for game-theoretically optimal strategies. Inspired by our solutions, we give a new paradigm for secure adaptive steganography, the so-called equalizer embedding strategies