  Due to data dependency and model leakage properties, Deep Neural Networks
(DNNs) exhibit several security vulnerabilities. Several security attacks
exploited them but most of them require the output probability vector. These
attacks can be mitigated by concealing the output probability vector. To
address this limitation, decision-based attacks have been proposed which can
estimate the model but they require several thousand queries to generate a
single untargeted attack image. However, in real-time attacks, resources and
attack time are very crucial parameters. Therefore, in resource-constrained
systems, e.g., autonomous vehicles where an untargeted attack can have a
catastrophic effect, these attacks may not work efficiently. To address this
limitation, we propose a resource efficient decision-based methodology which
generates the imperceptible attack, i.e., the RED-Attack, for a given black-box
model. The proposed methodology follows two main steps to generate the
imperceptible attack, i.e., classification boundary estimation and adversarial
noise optimization. Firstly, we propose a half-interval search-based algorithm
for estimating a sample on the classification boundary using a target image and
a randomly selected image from another class. Secondly, we propose an
optimization algorithm which first, introduces a small perturbation in some
randomly selected pixels of the estimated sample. Then to ensure
imperceptibility, it optimizes the distance between the perturbed and target
samples. For illustration, we evaluate it for CFAR-10 and German Traffic Sign
Recognition (GTSR) using state-of-the-art networks.
