In this paper, we investigate the problem of segmenting the foreground objects from the known background. The target application is a setting where a large display serves as the backdrop for a scene. The method proposed in this paper solves this problem in two steps. We try to separate the foreground and the background with the modified background subtraction base technique, which involves taking the per-pixel difference of the image with and without the foreground. The modification includes blurring the image before taking the difference and finding contours to separate noise and small objects in the background from the foreground. If the background subtraction fails, the chroma-key method is used: we put the solid colour screen behind the foreground object to ease the separation. To minimize the adverse effects of the chroma-key on the lighting of the objects, we propose the method for the background screen minimization using the pinhole camera theory.

Machine learning, a pivotal aspect of artificial intelligence, has dramatically altered our interaction with technology and our handling of extensive data. Through its ability to learn and make decisions from patterns and previous experiences, machine learning is growing in influence on different aspects of our lives. It is, however, shown that machine learning can be attacked, and by the attacks, its functioning may become completely opposite of what it was designed. A special kind of attack on machine learning models is a backdoor attack. It uses a special pattern that was placed in the training data by malicious users to alter the models’ behaviour. This pattern is called a backdoor trigger, and it can take any possible form. The test data with this trigger will be misclassified, while the clean data will get a correct prediction. This property makes the backdoor attacks stealthy and hard to detect. The backdoor attacks are mostly created to attack the classification models, where for each data sample, there is a label. In this thesis, we move away from the classification setup and create the first (to our knowledge) backdoor attack on the linear regression. We show that the triggers constructed using different versions of feature selection algorithms can be effective and impose a high error on the linear learning model prediction. Additionally, the study shows that backdoor attacks with the trigger constructed with a feature selection using correlation analysis lead to a higher error than the one using random forest for feature selection. Furthermore, we also transfer this backdoor attack to the federated learning setup. The results prove to be highly dependent on the number of poisoned nodes, while for all of them, the error for the poisoned region is higher than for the clean data. Finally, for the attack in both setups, we have adapted popular defence mechanisms that work against backdoor attacks on classification models. For the centralised setup, we have explored the possibility of using the studentized residuals as an outlier detection mechanism. The results are diverse, becoming worse when the poisoning rate of the model increases. To prevent the attacks in the federated setup, we used the FoolsGold defence mechanism, and it proved to be effective against the backdoor attacks on the regression model in all the cases except the one with exactly one attacking node.