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Deep neural networks obtain state-of-the-art performance on a series of tasks. However, they are easily fooled by adding a small adversarial perturbation to the input. The perturbation is often imperceptible to humans on image data. We observe a significant difference in feature attributions between adversarially crafted examples and original examples. Based on this observation, we introduce a new framework to detect adversarial examples through thresholding a scale estimate of feature attribution scores. Furthermore, we extend our method to include multi-layer feature attributions in order to tackle attacks that have mixed confidence levels. As demonstrated in extensive experiments, our method achieves superior performances in distinguishing adversarial examples from popular attack methods on a variety of real data sets compared to state-of-the-art detection methods. In particular, our method is able to detect adversarial examples of mixed confidence levels, and transfer between different attacking methods. We also show that our method achieves competitive performance even when the attacker has complete access to the detector. 

HIV-1 full-length RNA (HIV-1 RNA) plays a central role in viral replication, serving as a template for Gag/Gag-Pol translation and as a genome for the progeny virion. To gain a better understanding of the regulatory mechanisms of HIV-1 replication, we adapted a recently described system to visualize and track translation from individual HIV-1 RNA molecules in living cells. We found that, on average, half of the cytoplasmic HIV-1 RNAs are being actively translated at a given time. Furthermore, translating and nontranslating RNAs are well mixed in the cytoplasm; thus, Gag biogenesis occurs throughout the cytoplasm without being constrained to particular subcellular locations. Gag is an RNA binding protein that selects and packages HIV-1 RNA during virus assembly. A long-standing question in HIV-1 gene expression is whether Gag modulates HIV-1 RNA translation. We observed that despite its RNA-binding ability, Gag expression does not alter the proportion of translating HIV-1 RNA. Using single-molecule tracking, we found that both translating and nontranslating RNAs exhibit dynamic cytoplasmic movement and can reach the plasma membrane, the major HIV-1 assembly site. However, Gag selectively packages nontranslating RNA into the assembly complex. These studies illustrate that although HIV-1 RNA serves two functions, as a translation template and as a viral genome, individual RNA molecules carry out only one function at a time. These studies shed light on previously unknown aspects of HIV-1 gene expression and regulation.