Recent trojan attacks on deep neural network (DNN) models are one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker's chosen trojan trigger input. Trojan attacks are easy to craft; survive even in adverse conditions such as different viewpoints, and lighting conditions on images; and threaten real world applications such as autonomous vehicles and robotics. The trojan trigger is a secret guarded and exploited by the attacker. Therefore, detecting such trojaned inputs is a challenge, especially at run-time when models are in active operation. We focus on vision systems and build the STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of the predicted classes for the perturbed inputs for a given model---malicious or benign---under deployment. A low entropy in the predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input---a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on two popular and contrasting datasets: MNIST and CIFAR10. We achieve an overall false acceptance rate (FAR) of less than 1 , given a preset false rejection rate (FRR) of 1 , for four different tested trojan trigger types---three triggers are identified in previous attack works and one dedicated trigger is crafted by us to demonstrate the trigger-size insensitivity of the STRIP detection approach. In particular, on the dataset of natural images in CIFAR10, we have empirically achieved the desired result of 0 for both FRR and FAR.
Though there are general defenses against poisoning attacks @cite_20 , they cannot be immediately mounted to against trojan attacks. In practice, the user has no knowledge of the trojan trigger and no access to trojaned training sample, which make combating trojan attacks more challenging.
@cite_20
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Data poisoning is a type of adversarial attack on machine learning models wherein the attacker adds examples to the training set to manipulate the behavior of the model at test time. This paper explores a broad class of poisoning attacks on neural nets. The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the labeling of training data. They are also targeted; they control the behavior of the classifier on a specific test instance without noticeably degrading classifier performance on other instances. For example, an attacker could add a seemingly innocuous image (that is properly labeled) to a training set for a face recognition engine, and control the identity of a chosen person at test time. Because the attacker does not need to control the labeling function, poisons could be entered into the training set simply by putting them online and waiting for them to be scraped by a data collection bot. We present an optimization-based method for crafting poisons, and show that just one single poison image can control classifier behavior when transfer learning is used. For full end-to-end training, we present a "watermarking" strategy that makes poisoning reliable using multiple ( 50) poisoned training instances. We demonstrate our method by generating poisoned frog images from the CIFAR dataset and using them to manipulate image classifiers.