The unprecedented success of deep neural networks in various applications have made these networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs). We introduce the concept of Universal Litmus Patterns (ULPs), which enable one to reveal backdoor attacks by feeding these universal patterns to the network and analyzing the output (i.e., classifying as clean' or corrupted'). This detection is fast because it requires only a few forward passes through a CNN. We demonstrate the effectiveness of ULPs for detecting backdoor attacks on thousands of networks trained on three benchmark datasets, namely the German Traffic Sign Recognition Benchmark (GTSRB), MNIST, and CIFAR10.
The aforementioned defenses rely on two crucial assumptions: 1) the outliers in the clean dataset (non-infected) do not have a strong effect on the model and 2) more importantly, the user has access to the infected training dataset. These assumptions could be valid to specific scenarios, for instance, when the user trains her his own model based on the dataset provided by a third party. However, in a setting where the user outsources the model training to an untrusted third party, for instance, a Machine Learning as a Service (MLaaS) service provider, or when the user downloads a pre-trained model from an untrusted source, the assumption of having access to infected dataset is not valid. Recently, there has been several work that consider this very case, in which the user has access only to the model and clean data @cite_21 @cite_29 .
@cite_29 @cite_21
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As machine learning (ML) applications become increasingly prevalent, protecting the confidentiality of ML models becomes paramount for two reasons: (a) models may constitute a business advantage to its owner, and (b) an adversary may use a stolen model to find transferable adversarial examples that can be used to evade classification by the original model. One way to protect model confidentiality is to limit access to the model only via well-defined prediction APIs. This is common not only in machine-learning-as-a-service (MLaaS) settings where the model is remote, but also in scenarios like autonomous driving where the model is local but direct access to it is protected, for example, by hardware security mechanisms. Nevertheless, prediction APIs still leak information so that it is possible to mount model extraction attacks by an adversary who repeatedly queries the model via the prediction API. In this paper, we describe a new model extraction attack by combining a novel approach for generating synthetic queries together with recent advances in training deep neural networks. This attack outperforms state-of-the-art model extraction techniques in terms of transferability of targeted adversarial examples generated using the extracted model (+15-30 percentage points, pp), and in prediction accuracy (+15-20 pp) on two datasets. We then propose the first generic approach to effectively detect model extraction attacks: PRADA. It analyzes how the distribution of consecutive queries to the model evolves over time and raises an alarm when there are abrupt deviations. We show that PRADA can detect all known model extraction attacks with a 100 success rate and no false positives. PRADA is particularly suited for detecting extraction attacks against local models. Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantage to its owner, and (b) an adversary may use a stolen model to find transferable adversarial examples that can evade classification by the original model. Access to the model can be restricted to be only via well-defined prediction APIs. Nevertheless, prediction APIs still provide enough information to allow an adversary to mount model extraction attacks by sending repeated queries via the prediction API. In this paper, we describe new model extraction attacks using novel approaches for generating synthetic queries, and optimizing training hyperparameters. Our attacks outperform state-of-the-art model extraction in terms of transferability of both targeted and non-targeted adversarial examples (up to +29-44 percentage points, pp), and prediction accuracy (up to +46 pp) on two datasets. We provide take-aways on how to perform effective model extraction attacks. We then propose PRADA, the first step towards generic and effective detection of DNN model extraction attacks. It analyzes the distribution of consecutive API queries and raises an alarm when this distribution deviates from benign behavior. We show that PRADA can detect all prior model extraction attacks with no false positives. Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some of its training data; careful analysis of the model may therefore reveal sensitive information. @PARASPLIT To address this problem, we demonstrate a generally applicable approach to providing strong privacy guarantees for training data: Private Aggregation of Teacher Ensembles (PATE). The approach combines, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users. Because they rely directly on sensitive data, these models are not published, but instead used as ''teachers'' for a ''student'' model. The student learns to predict an output chosen by noisy voting among all of the teachers, and cannot directly access an individual teacher or the underlying data or parameters. The student's privacy properties can be understood both intuitively (since no single teacher and thus no single dataset dictates the student's training) and formally, in terms of differential privacy. These properties hold even if an adversary can not only query the student but also inspect its internal workings. @PARASPLIT Compared with previous work, the approach imposes only weak assumptions on how teachers are trained: it applies to any model, including non-convex models like DNNs. We achieve state-of-the-art privacy utility trade-offs on MNIST and SVHN thanks to an improved privacy analysis and semi-supervised learning. While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern. In particular, ML models are often trained on data from potentially untrustworthy sources, providing adversaries with the opportunity to manipulate them by inserting carefully crafted samples into the training set. Recent work has shown that this type of attack, called a poisoning attack, allows adversaries to insert backdoors or trojans into the model, enabling malicious behavior with simple external backdoor triggers at inference time and only a blackbox perspective of the model itself. Detecting this type of attack is challenging because the unexpected behavior occurs only when a backdoor trigger, which is known only to the adversary, is present. Model users, either direct users of training data or users of pre-trained model from a catalog, may not guarantee the safe operation of their ML-based system. In this paper, we propose a novel approach to backdoor detection and removal for neural networks. Through extensive experimental results, we demonstrate its effectiveness for neural networks classifying text and images. To the best of our knowledge, this is the first methodology capable of detecting poisonous data crafted to insert backdoors and repairing the model that does not require a verified and trusted dataset.