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.
Similar to @cite_21 @cite_29 , we also seek an approach for detection of backdoor attacks without the need for the infected training data. We, however, approach the problem from a different angle. In short, we learn universal and transferable set of patterns that serve as a Litmus test for identifying networks containing backdoor Trojan attacks, hence we call them Universal Litmus Patterns. To detect whether a network is poisoned or not, the ULPs are fed through the network and the corresponding outputs (i.e., Logits) are linearly classified to reveal backdoor attacks.
@cite_29 @cite_21
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Malicious tampering of the internal circuits of ICs can lead to detrimental results. Insertion of Trojan circuits may change system behavior, cause chip failure or send information to a third party. This paper presents an information-theoretic approach for Trojan detection. It estimates the statistical correlation between the signals in a design, and explores how this estimation can be used in a clustering algorithm to detect the Trojan logic. Compared with the other algorithms, our tool does not require extensive logic analysis. We neither need the circuit to be brought to the triggering state, nor the effect of the Trojan payload to be propagated and observed at the output. Instead we leverage already available simulation data in this information-theoretic approach. We conducted experiments on the TrustHub benchmarks to validate the practical efficacy of this approach. The results show that our tool can detect Trojan logic with up to 100 coverage with low false positive rates. 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.