Public code platforms like GitHub are exposed to several different attacks, and in particular to the detection and exploitation of sensitive information (such as passwords or API keys). While both developers and companies are aware of this issue, there is no efficient open-source tool performing leak detection with a significant precision rate. Indeed, a common problem in leak detection is the amount of false positive data (ie, non critical data wrongly detected as a leak), leading to an important workload for developers manually reviewing them. This paper presents an approach to detect data leaks in open-source projects with a low false positive rate. In addition to regular expression scanners commonly used by current approaches, we propose several machine learning models targeting the false positives, showing that current approaches generate an important false positive rate close to 80%. Furthermore, we demonstrate that our tool, while producing a negligible false negative rate, decreases the false positive rate to, at most, 6% of the output data.

With the development of machine learning models for task automation, watermarking appears to be a suitable solution to protect one’s own intellectual property. Indeed, by embedding secret specific markers into the model, the model owner is able to analyze the behavior of any model on these markers, called trigger instances and hence claim its ownership if this is the case. However, in the context of a Machine Learning as a Service (MLaaS) platform where models are available for inference, an attacker could forge such proofs in order to steal the ownership of these watermarked models in order to make a profit out of it. This type of attacks, called watermark forging attacks, is a serious threat against the intellectual property of models owners. Current work provides limited solutions to this problem: They constrain model owners to disclose either their models or their trigger set to a third party. In this paper, we propose counter-measures against watermark forging attacks, in a black-box environment and compatible with privacy-preserving machine learning where both the model weights and the inputs could be kept private. We show that our solution successfully prevents two different types of watermark forging attacks with minimalist assumptions regarding either the access to the model’s weight or the content of the trigger set.

Since machine learning models have become a valuable asset for companies, watermarking techniques have been developed to protect the intellectual property of these models and prevent model theft. We observe that current watermarking frameworks solely target image classification tasks, neglecting a considerable part of machine learning techniques. In this paper, we propose to address this lack and study the watermarking process of various machine learning techniques such as machine translation, regression, binary image classification and reinforcement learning models. We adapt current definitions to each specific technique and we evaluate the main characteristics of the watermarking process, in particular the robustness of the models against a rational adversary. We show that watermarking models beyond classification is possible while preserving their overall performance. We further investigate various attacks and discuss the importance of the performance metric in the verification process and its impact on the success of the adversary.