RESUMEN
Deep neural networks (DNNs) are increasingly being used in malware detection and their robustness has been widely discussed. Conventionally, the development of an adversarial example generation scheme for DNNs involves either detailed knowledge concerning the model (i.e., gradient-based methods) or a substantial quantity of data for training a surrogate model. However, under many real-world circumstances, neither of these resources is necessarily available. Our work introduces the concept of the instance-based attack, which is both interpretable and suitable for deployment in a black-box environment. In our approach, a specific binary instance and a malware classifier are utilized as input. By incorporating data augmentation strategies, sufficient data are generated to train a relatively simple and interpretable model. Our methodology involves providing explanations for the detection model, which entails displaying the weights assigned to different components of the specific binary. Through the analysis of these explanations, we discover that the data subsections have a significant impact on the identification of malware. In this study, a novel function preserving transformation algorithm designed specifically for data subsections is introduced. Our approach involves leveraging binary diversification techniques to neutralize the effects of the most heavily-weighted section, thus generating effective adversarial examples. Our algorithm can fool the DNNs in certain cases with a success rate of almost 100%. Instance attack exhibits superior performance compared to the state-of-the-art approach. Notably, our technique can be implemented in a black-box environment and the results can be verified utilizing domain knowledge. The model can help to improve the robustness of malware detectors.
RESUMEN
Background subtraction is a classic video processing task pervading in numerous visual applications such as video surveillance and traffic monitoring. Given the diversity and variability of real application scenes, an ideal background subtraction model should be robust to various scenarios. Even though deep-learning approaches have demonstrated unprecedented improvements, they often fail to generalize to unseen scenarios, thereby less suitable for extensive deployment. In this work, we propose to tackle cross-scene background subtraction via a two-phase framework that includes meta-knowledge learning and domain adaptation. Specifically, as we observe that meta-knowledge (i.e., scene-independent common knowledge) is the cornerstone for generalizing to unseen scenes, we draw on traditional frame differencing algorithms and design a deep difference network (DDN) to encode meta-knowledge especially temporal change knowledge from various cross-scene data (source domain) without intermittent foreground motion pattern. In addition, we explore a self-training domain adaptation strategy based on iterative evolution. With iteratively updated pseudo-labels, the DDN is continuously fine-tuned and evolves progressively toward unseen scenes (target domain) in an unsupervised fashion. Our framework could be easily deployed on unseen scenes without relying on their annotations. As evidenced by our experiments on the CDnet2014 dataset, it brings a significant improvement to background subtraction. Our method has a favorable processing speed (70 fps) and outperforms the best unsupervised algorithm and top supervised algorithm designed for unseen scenes by 9% and 3%, respectively.