# TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks Source: [TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:39:16.015Z Authors: Chandramouli Amarnath, Aishwarya H. Balwani, Kwondo Ma, Abhijit Chatterjee Published: 2021-10-16T02:10:36.000Z ## Abstract Deep neural networks (DNNs) are now the de facto choice for computer vision tasks such as image classification. However, their complexity and "black box" nature often renders the systems they're deployed in vulnerable to a range of security threats. Successfully identifying such threats, especially in safety-critical real-world applications is thus of utmost importance, but still very much an open problem. We present TESDA, a low-overhead, flexible, and statistically grounded method for {online detection} of attacks by exploiting the discrepancies they cause in the distributions of intermediate layer features of DNNs. Unlike most prior work, we require neither dedicated hardware to run in real-time, nor the presence of a Trojan trigger to detect discrepancies in behavior. We empirically establish our method's usefulness and practicality across multiple architectures, datasets and diverse attacks, consistently achieving detection coverages of above 95% with operation count overheads as low as 1-2%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()