Steganographer Identification

Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users called actors are guilty of using steganography, which is typically defined as the Steganographer Identification Problem (SIP). One might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection, the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when non-adaptive steganographic embedding was applied. In this chapter, we will present foundational concepts and review advanced methodologies in SIP. This chapter is self-contained and intended as a tutorial introducing the SIP in the context of media steganography.Comment: A tutorial with 30 page

Classification with Costly Features using Deep Reinforcement Learning

We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost. We revisit a former approach that has framed the problem as a sequential decision-making problem and solved it by Q-learning with a linear approximation, where individual actions are either requests for feature values or terminate the episode by providing a classification decision. On a set of eight problems, we demonstrate that by replacing the linear approximation with neural networks the approach becomes comparable to the state-of-the-art algorithms developed specifically for this problem. The approach is flexible, as it can be improved with any new reinforcement learning enhancement, it allows inclusion of pre-trained high-performance classifier, and unlike prior art, its performance is robust across all evaluated datasets.Comment: AAAI 201

Penzion for seniors

Diplomová práce řeší projektovou dokumentaci Penzionu pro seniory ve městě Humpolec. Dům je určen pro bydlení 16 - 18 osob. Součástí objektu je kavárna a rukodělná dílna. Dům má tři nadzemní podlaží a je částečně podsklepený. Objekt je zastřešen pomocí dvou pultových a jedné ploché střechy. Stavební pozemek je mírně svažitý k severovýchodu.Master's thesis addresses the design documentation Pension for seniors in Humpolec. The house is designed for housing 16-18 people. There is a café and an artisanal workshop. The house has three floors and a partial basement. The building is covered with two pent roof and a flat roof. Building land is gently sloping to the northeast.

Family house Rozvadze

Predmetom tejto bakalárskej práce je vypracovanie projektovej dokumentácie k vytvoreniu novostavby rodinného domu v katastrálnom území obce Trenčianske Stankovce, časť Rozvadze. Rodinný dom je samostatne stojaci dvojpodlažný objekt na okraji obce. Objekt je jednogeneračný, navrhnutý pre 4–5 osôb. Súčasťou objektu je garáž s jedným státím. Pôdorysný tvar objektu pripomína písmeno T. Konštrukčný systém je pozdĺžny, stenový. Zvislé a vodorovné konštrukcie sú zo systému HELUZ. Zastrešenie objektu je riešené plochými strechami. Nad časťou objektu je extenzívna zelená strecha. Výkresová časť práce je spracovaná počítačovým programom AutoCAD.The subject of this bachelor thesis is working out the project documentation to execution of a new detached house in cadastral area of Trenčianske Stankovce, part Rozvadze. Family house is detached two-storeyed building, located at the edge of the village. Object is meant for one generation, designed for 4-5 people. The building includes a garage with one car stand. Floor plan is designed in shape of a latter T. Construction system is longitudinal, wall made. Vertical and horizontal supporting structures of the house are made by the system HELUZ. Roofing of the building is designed with flat roofs. On the part of the object is extensive green roof. The drawing part is handled by the AutoCAD software.

Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks

We focus on reinforcement learning (RL) in relational problems that are naturally defined in terms of objects, their relations, and manipulations. These problems are characterized by variable state and action spaces, and finding a fixed-length representation, required by most existing RL methods, is difficult, if not impossible. We present a deep RL framework based on graph neural networks and auto-regressive policy decomposition that naturally works with these problems and is completely domain-independent. We demonstrate the framework in three very distinct domains and we report the method's competitive performance and impressive zero-shot generalization over different problem sizes. In goal-oriented BlockWorld, we demonstrate multi-parameter actions with pre-conditions. In SysAdmin, we show how to select multiple objects simultaneously. In the classical planning domain of Sokoban, the method trained exclusively on 10x10 problems with three boxes solves 89% of 15x15 problems with five boxes.Comment: RL4RealLife @ ICML2021; code available at https://github.com/jaromiru/sr-dr

Hierarchical Multiple-Instance Data Classification with Costly Features

We extend the framework of Classification with Costly Features (CwCF) that works with samples of fixed dimensions to trees of varying depth and breadth (similar to a JSON/XML file). In this setting, the sample is a tree - sets of sets of features. Individually for each sample, the task is to sequentially select informative features that help the classification. Each feature has a real-valued cost, and the objective is to maximize accuracy while minimizing the total cost. The process is modeled as an MDP where the states represent the acquired features, and the actions select unknown features. We present a specialized neural network architecture trained through deep reinforcement learning that naturally fits the data and directly selects features in the tree. We demonstrate our method in seven datasets and compare it to two baselines.Comment: RL4RealLife @ ICML2021; code available at https://github.com/jaromiru/rcwc

NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios

Current frameworks for training offensive penetration testing agents with deep reinforcement learning struggle to produce agents that perform well in real-world scenarios, due to the reality gap in simulation-based frameworks and the lack of scalability in emulation-based frameworks. Additionally, existing frameworks often use an unrealistic metric that measures the agents' performance on the training data. NASimEmu, a new framework introduced in this paper, addresses these issues by providing both a simulator and an emulator with a shared interface. This approach allows agents to be trained in simulation and deployed in the emulator, thus verifying the realism of the used abstraction. Our framework promotes the development of general agents that can transfer to novel scenarios unseen during their training. For the simulation part, we adopt an existing simulator NASim and enhance its realism. The emulator is implemented with industry-level tools, such as Vagrant, VirtualBox, and Metasploit. Experiments demonstrate that a simulation-trained agent can be deployed in emulation, and we show how to use the framework to train a general agent that transfers into novel, structurally different scenarios. NASimEmu is available as open-source.Comment: NASimEmu is available at https://github.com/jaromiru/NASimEmu and the baseline agents at https://github.com/jaromiru/NASimEmu-agent