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Found 4 papers, 1 papers with code
HDTest: Differential Fuzz Testing of Brain-Inspired Hyperdimensional Computing
Using handwritten digit classification as an example, we show that HDTest can generate thousands of adversarial inputs with negligible perturbations that can successfully fool HDC models.
RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems
Compared with the neuron coverage, the proposed state coverage metrics as guidance excel with 4. 17% to 97. 22% higher success (or generation) rate.
LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment through Program Metrics
The latter needs the prior knowledge of known vulnerabilities and can only identify similar but not new types of vulnerabilities.
Software Engineering
DLFuzz: Differential Fuzzing Testing of Deep Learning Systems
Deep learning (DL) systems are increasingly applied to safety-critical domains such as autonomous driving cars.
Software Engineering
