The non-zero-sum game of steganography in heterogeneous environments

The highly heterogeneous nature of images found in real-world environments, such as online sharing platforms, has been one of the long-standing obstacles to the transition of steganalysis techniques outside the laboratory. Recent advances in identifying the properties of images relevant to steganalysis as well as the effectiveness of deep neural networks on highly heterogeneous datasets have laid some groundwork for resolving this problem. Despite this progress, we argue that the way the game played between the steganographer and the steganalyst is currently modeled lacks some important features expected in a real-world environment: 1) the steganographer can adapt her cover source choice to the environment and/or to the steganalyst’s classifier, 2) the distribution of cover sources in the environment impacts the optimal threshold for a given classifier, and 3) the steganalyst and steganographer have different goals, hence different utilities. We propose to take these facts into account using a two-player non-zero-sum game constrained by an environment composed of multiple cover sources. We then show how to convert this non-zero-sum game into an equivalent zero-sum game, allowing us to propose two methods to find Nash equilibria for this game: a standard method using the double oracle algorithm and a minimum regret method based on approximating a set of atomistic classifiers. Applying these methods to contemporary steganography and steganalysis in a realistic environment, we show that classifiers which do not adapt to the environment severely underperform when the steganographer is allowed to select into which cover source to embed

An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection

As the field of image processing and computer vision continues to develop, we are able to create edited images that seem more natural than ever before. Identifying real photos from fakes has become a formidable obstacle. Image forgery has become more common as the multimedia capabilities of personal computers have developed over the previous several years. This is due to the fact that it is simpler to produce fake images. Since image object fabrication might obscure critical evidence, techniques for detecting it have been intensively investigated for quite some time. The publicly available datasets are insufficient to deal with these problems adequately. Our work recommends using a deep learning based image inpainting technique to create a model to detect fabricated images. To further detect copy-move forgeries in images, we use an CNN-LSTM and Improved VGG adaptation network. Our approach could be useful in cases when classifying the data is impossible. In contrast, researchers seldom use deep learning theory, preferring instead to depend on tried-and-true techniques like image processing and classifiers. In this article, we recommend the CNN-LSTM and improved VGG-16 convolutional neural network for intra-frame forensic analysis of altered images

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

An examination of the Asus WL-HDD 2.5 as a nepenthes malware collector

The Linksys WRT54g has been used as a host for network forensics tools for instance Snort for a long period of time. Whilst large corporations are already utilising network forensic tools, this paper demonstrates that it is quite feasible for a non-security specialist to track and capture malicious network traffic. This paper introduces the Asus Wireless Hard disk as a replacement for the popular Linksys WRT54g. Firstly, the Linksys router will be introduced detailing some of the research that was undertaken on the device over the years amongst the security community. It then briefly discusses malicious software and the impact this may have for a home user. The paper then outlines the trivial steps in setting up Nepenthes 0.1.7 (a malware collector) for the Asus WL-HDD 2.5 according to the Nepenthes and tests the feasibility of running the malware collector on the selected device. The paper then concludes on discussing the limitations of the device when attempting to execute Nepenthes

Blockchain-based secure and efficient secret image sharing with outsourcing computation in wireless networks

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the ( k, n ) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks

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

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Air Force Institute of Technology Research Report 2006

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area