Deep Learning for Reversible Steganography: Principles and Insights

Deep-learning\textendash{centric} reversible steganography has emerged as a promising research paradigm. A direct way of applying deep learning to reversible steganography is to construct a pair of encoder and decoder, whose parameters are trained jointly, thereby learning the steganographic system as a whole. This end-to-end framework, however, falls short of the reversibility requirement because it is difficult for this kind of monolithic system, as a black box, to create or duplicate intricate reversible mechanisms. In response to this issue, a recent approach is to carve up the steganographic system and work on modules independently. In particular, neural networks are deployed in an analytics module to learn the data distribution, while an established mechanism is called upon to handle the remaining tasks. In this paper, we investigate the modular framework and deploy deep neural networks in a reversible steganographic scheme referred to as prediction-error modulation, in which an analytics module serves the purpose of pixel intensity prediction. The primary focus of this study is on deep-learning\textendash{based} context-aware pixel intensity prediction. We address the unsolved issues reported in related literature, including the impact of pixel initialisation on prediction accuracy and the influence of uncertainty propagation in dual-layer embedding. Furthermore, we establish a connection between context-aware pixel intensity prediction and low-level computer vision and analyse the performance of several advanced neural networks

Copyright Protection for Surveillance System Multimedia Stream with Cellular Automata Watermarking

Intelligent Surveillance Systems are attracting extraordinary attention from research and industry. Security and privacy protection are critical issues for public acceptance of security camera networks. Existing approaches, however, only address isolated aspects without considering the integration with established security technologies and the underlying platform. Easy availability of internet, together with relatively inexpensive digital recording and storage peripherals has created an era where duplication, unauthorized use and misdistribution of digital content has become easier. The ease of availability made digital video popular over analog media like film or tape. At the same time it demands a sharp attention regarding the ownership issue. The ownership and integrity can easily be violated using different audio and video editing softwares. To prevent unauthorized use, misappropriation, misrepresentation; authentication of multimedia contents achieved a broad attention in recent days and to achieve secure copyright protection we embedded some information in audio and videos and that audio or video is called copyright protected. Digital watermarking is a technology to embed additional information into the host signal to ensure security and protection of multimedia data. The embedded information can’t be detected by human but some attacks and operations can tamper that information to breach protection. So in order to find a secure technique of copyright protection, we have analyzed different techniques. After having a good understanding of these techniques we have proposed a novel algorithm that generates results with high effectiveness, additionally we can use self-extracted watermark technique to increase the security and automate the process of watermarking. Forensic digital watermarking is a promising tool in the fight against piracy of copyrighted motion imagery content, but to be effective it must be (1) imperceptibly embedded in high-definition motion picture source, (2) reliably retrieved, even from degraded copies as might result from camcorder capture and subsequent very-low-bitrate compression and distribution on the Internet, and (3) secure against unauthorized removal. Audio and video watermarking enables the copyright protection with owner or customer authentication and the detection of media manipulations. The available watermarking technology concentrates on single media like audio or video. But the typical multimedia stream consists of both video and audio data. Our goal is to provide a solution with robust and fragile aspects to guarantee authentication and integrity by using watermarks in combination with content information. We show two solutions for the protection of audio and video data with a combined robust and fragile watermarking approach. The first solution is to insert a time code into the data: We embed a signal as a watermark to detect gaps or changes in the flow of time. The second solution is more complex: We use watermarks to embed information in each media about the content of the other media. In our paper we present the problem of copyright protection and integrity checks for combined video and audio data. Both the solutions depend upon cellular automata, cellular automata are a powerful computation model that provides a simple way to simulate and solve many difficult problems in different fields. The most widely known example of Cellular Automata is the Game-of-Life. Cellular automaton growth is controlled by predefined rule or programs .The rule describes how the cell will interact with its neighborhood. Once the automaton is started it will work on its own according to the rule specified.

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks

The commercial use of Machine Learning (ML) is spreading; at the same time, ML models are becoming more complex and more expensive to train, which makes Intellectual Property Protection (IPP) of trained models a pressing issue. Unlike other domains that can build on a solid understanding of the threats, attacks and defenses available to protect their IP, the ML-related research in this regard is still very fragmented. This is also due to a missing unified view as well as a common taxonomy of these aspects. In this paper, we systematize our findings on IPP in ML, while focusing on threats and attacks identified and defenses proposed at the time of writing. We develop a comprehensive threat model for IP in ML, categorizing attacks and defenses within a unified and consolidated taxonomy, thus bridging research from both the ML and security communities

Facial re-enactment, speech synthesis and the rise of the Deepfake

Emergent technologies in the fields of audio speech synthesis and video facial manipulation have the potential to drastically impact our societal patterns of multimedia consumption. At a time when social media and internet culture is plagued by misinformation, propaganda and “fake news”, their latent misuse represents a possible looming threat to fragile systems of information sharing and social democratic discourse. It has thus become increasingly recognised in both academic and mainstream journalism that the ramifications of these tools must be examined to determine what they are and how their widespread availability can be managed. This research project seeks to examine four emerging software programs – Face2Face, FakeApp , Adobe VoCo and Lyrebird – that are designed to facilitate the synthesis of speech and manipulate facial features in videos. I will explore their positive industry applications and the potentially negative consequences of their release into the public domain. Consideration will be directed to how such consequences and risks can be ameliorated through detection, regulation and education. A final analysis of these three competing threads will then attempt to address whether the practical and commercial applications of these technologies are outweighed by the inherent unethical or illegal uses they engender, and if so; what we can do in response

Authenticated public key elliptic curve based on deep convolutional neural network for cybersecurity image encryption application

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the (Formula presented.) parameters between two parties by using the EC-DHKE algorithm. Moreover, the 3D Quantum Chaotic Logistic Map (3D QCLM) has an extremely chaotic behavior of the bifurcation diagram and high Lyapunov exponent, which can be used in high-level security. In addition, in order to achieve the authentication property, the secure hash function uses the output sequence of the DCNN and the output sequence of the 3D QCLM in the proposed authenticated expansion diffusion matrix (AEDM). Finally, partial frequency domain encryption (PFDE) technique is achieved by using the discrete wavelet transform in order to satisfy the robustness and fast encryption process. Simulation results and security analysis demonstrate that the proposed encryption algorithm achieved the performance of the state-of-the-art techniques in terms of quality, security, and robustness against noise- and signal-processing attacks

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Smart techniques and tools to detect Steganography - a viable practice to Security Office Department

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementInternet is today a commodity and a way for being connect to the world. It is through Internet is where most of the information is shared and where people run their businesses. However, there are some people that make a malicious use of it. Cyberattacks have been increasing all over the recent years, targeting people and organizations, looking to perform illegal actions. Cyber criminals are always looking for new ways to deliver malware to victims to launch an attack. Millions of users share images and photos on their social networks and generally users find them safe to use. Contrary to what most people think, images can contain a malicious payload and perform harmful actions. Steganography is the technique of hiding data, which, combined with media files, can be used to place malicious code. This problem, leveraged by the continuous media file sharing through massive use of digital platforms, may become a worldwide threat in malicious content sharing. Like phishing, people and organizations must be trained to suspect about inappropriate content and implement the proper set of actions to reduce probability of infections when accessing files supposed to be inoffensive. The aim of this study will try to help people and organizations by trying to set a toolbox where it can be possible to get some tools and techniques to assist in dealing with this kind of situations. A theoretical overview will be performed over other concepts such as Steganalysis, touching also Deep Learning and in Machine Learning to assess which is the range of its applicability in find solutions in detection and facing these situations. In addition, understanding the current main technologies, architectures and users’ hurdles will play an important role in designing and developing the proposed toolbox artifact

TORKAMELEON. IMPROVING TOR’S CENSORSHIP RESISTANCE WITH K-ANONYMIZATION MEDIA MORPHING COVERT INPUT CHANNELS

Anonymity networks such as Tor and other related tools are powerful means of increas- ing the anonymity and privacy of Internet users’ communications. Tor is currently the most widely used solution by whistleblowers to disclose confidential information and denounce censorship measures, including violations of civil rights, freedom of expres- sion, or guarantees of free access to information. However, recent research studies have shown that Tor is vulnerable to so-called powerful correlation attacks carried out by global adversaries or collaborative Internet censorship parties. In the Tor ”arms race” scenario, we can see that as new censorship, surveillance, and deep correlation tools have been researched, new, improved solutions for preserving anonymity have also emerged. In recent research proposals, unobservable encapsulation of IP packets in covert media channels is one of the most promising defenses against such threat models. They leverage WebRTC-based covert channels as a robust and practical approach against powerful traf- fic correlation analysis. At the same time, these solutions are difficult to combat through the traffic-blocking measures commonly used by censorship authorities. In this dissertation, we propose TorKameleon, a censorship evasion solution de- signed to protect Tor users with increased censorship resistance against powerful traffic correlation attacks executed by global adversaries. The system is based on flexible K- anonymization input circuits that can support TLS tunneling and WebRTC-based covert channels before forwarding users’ original input traffic to the Tor network. Our goal is to protect users from machine and deep learning correlation attacks between incom- ing user traffic and observed traffic at different Tor network relays, such as middle and egress relays. TorKameleon is the first system to implement a Tor pluggable transport based on parameterizable TLS tunneling and WebRTC-based covert channels. We have implemented the TorKameleon prototype and performed extensive validations to ob- serve the correctness and experimental performance of the proposed solution in the Tor environment. With these evaluations, we analyze the necessary tradeoffs between the performance of the standard Tor network and the achieved effectiveness and performance of TorKameleon, capable of preserving the required unobservability properties.Redes de anonimização como o Tor e soluções ou ferramentas semelhantes são meios poderosos de aumentar a anonimidade e a privacidade das comunicações de utilizadores da Internet . O Tor é atualmente a rede de anonimato mais utilizada por delatores para divulgar informações confidenciais e denunciar medidas de censura tais como violações de direitos civis e da liberdade de expressão, ou falhas nas garantias de livre acesso à informação. No entanto, estudos recentes mostram que o Tor é vulnerável a adversários globais ou a entidades que colaboram entre si para garantir a censura online. Neste cenário competitivo e de jogo do “gato e do rato”, é possível verificar que à medida que novas soluções de censura e vigilância são investigadas, novos sistemas melhorados para a preservação de anonimato são também apresentados e refinados. O encapsulamento de pacotes IP em túneis encapsulados em protocolos de media são uma das mais promissoras soluções contra os novos modelos de ataque à anonimidade. Estas soluções alavancam canais encobertos em protocolos de media baseados em WebRTC para resistir a poderosos ataques de correlação de tráfego e a medidas de bloqueios normalmente usadas pelos censores. Nesta dissertação propomos o TorKameleon, uma solução desenhada para protoger os utilizadores da rede Tor contra os mais recentes ataques de correlação feitos por um modelo de adversário global. O sistema é baseado em estratégias de anonimização e reencaminhamento do tráfego do utilizador através de K nós, utilizando também encap- sulamento do tráfego em canais encobertos em túneis TLS ou WebRTC. O nosso objetivo é proteger os utilizadores da rede Tor de ataques de correlação implementados através de modelos de aprendizagem automática feitos entre o tráfego do utilizador que entra na rede Tor e esse mesmo tráfego noutro segmento da rede, como por exemplo nos nós de saída da rede. O TorKameleon é o primeiro sistema a implementar um Tor pluggable transport parametrizável, baseado em túneis TLS ou em canais encobertos em protocolos media. Implementamos um protótipo do sistema e realizamos uma extensa avalição expe- rimental, inserindo a solução no ambiente da rede Tor. Com base nestas avaliações, anali- zamos o tradeoff necessário entre a performance da rede Tor e a eficácia e a performance obtida do TorKameleon, que garante as propriedades de preservação de anonimato