The security of a steganography system is defined by our ability to detect it. It is of no surprise then that steganography and steganalysis both depend heavily on the accuracy and robustness of our detectors. This is especially true when real-world data is considered, due to its heterogeneity. The difficulty of such data manifests itself in a penalty that has periodically been reported to affect the performance of detectors built on binary classifiers; this is known as cover source mismatch. It remains unclear how the performance drop that is associated with cover source mismatch is mitigated or even measured. In this thesis we aim to show a robust methodology to empirically measure its effects on the detection accuracy of steganalysis classifiers. Some basic machine-learning based methods, which take their origin in domain adaptation, are proposed to counter it. Specifically, we test two hypotheses through an empirical investigation. First, that linear classifiers are more robust than non-linear classifiers to cover source mismatch in real-world data and, second, that linear classifiers are so robust that given sufficiently large mismatched training data they can equal the performance of any classifier trained on small matched data. With the help of theory we draw several nontrivial conclusions based on our results. The penalty from cover source mismatch may, in fact, be a combination of two types of error; estimation error and adaptation error. We show that relatedness between training and test data, as well as the choice of classifier, both have an impact on adaptation error, which, as we argue, ultimately defines a detector's robustness. This provides a novel framework for reasoning about what is required to improve the robustness of steganalysis detectors. Whilst our empirical results may be viewed as the first step towards this goal, we show that our approach provides clear advantages over earlier methods. To our knowledge this is the first study of this scale and structure.

Successful applications of Reinforcement Learning (RL) in the robotics field has proliferated after DeepMind and OpenAI showed the ability of RL techniques to develop intelligent robotic systems that could learn to perform complex tasks. Ever since the use of robots for surgical procedures, researchers have been trying to bring some sort of autonomy into the operating room. Surgical robotic systems such as da Vinci currently provide the surgeons with direct control. To relieve the stress and the burden on the surgeon using the da Vinci robot, semi-automating or automating surgical tasks such as suturing can be beneficial. This work presents a RL-based approach to automate the needle hand-off task. It puts forward two approaches based on the type of environment, a discrete and continuous space approach. For capturing a unique suturing style, user data was collected using the da Vinci Research Kit to generate a sparse reward function. It was used to derive an optimal policy using Q-learning for a discretized environment. Further, a RL framework for da Vinci Research Kit was developed using a real-time dynamics simulator - Asynchronous Multi-Body Framework (AMBF). A model was trained and evaluated to reach the desired goal using model-free RL techniques while considering the dynamics of the robot to help mitigate the difficulty in transferring trained model to real-world robots. Therefore, the developed RL framework would enable the RL community to train surgical robots using state of the art RL techniques and transfer it to real-world robots with minimal effort. Based on the results obtained, the viability of applying RL techniques to develop a supervised level of autonomy for performing surgical tasks is discussed. To summarize, this work mainly focuses on using RL to automate the suture hand-off task in order to move a step towards solving the greater problem of automating suturing.