The interplay between machine learning and security is becoming more prominent. New applications using machine learning also bring new security risks. Here, we show it is possible to reverse-engineer the inputs to a neural network with only a single-...

Over 8 million users rely on the Tor network each day to protect their anonymity online. Unfortunately, Tor has been shown to be vulnerable to the website fingerprinting attack, which allows an attacker to deduce the website a user is visiting based on ...

With the advent of the Internet, social media platforms have become an increasingly popular medium of communication for people. Platforms like Twitter and Quora allow people to express their opinions on a large scale. These platforms are, however, ...

There is heightened concern over deliberately inaccurate news. Recently, so-called deepfake videos and images that are modified by or generated by artificial intelligence techniques have become more realistic and easier to create. These techniques could ...

Many companies offer automatic speech recognition or Speech-to-Text APIs for use in diverse applications. However, audio classification algorithms trained with deep neural networks (DNNs) can sometimes misclassify adversarial examples, posing a ...

Machine learning has proved to be a promising technology to determine whether a piece of software is malicious or benign. However, the accuracy of this approach comes sometimes at the expense of its robustness and probing these systems against ...

This paper presents a technique to scan neural network based AI models to determine if they are trojaned. Pre-trained AI models may contain back-doors that are injected through training or by transforming inner neuron weights. These trojaned models ...

Access control is often reported to be "profoundly broken" in real-world practices due to prevalent policy misconfigurations introduced by system administrators (sysadmins). Given the dynamics of resource and data sharing, access control policies need ...

Researchers and censoring regimes have long engaged in a cat-and-mouse game, leading to increasingly sophisticated Internet-scale censorship techniques and methods to evade them. In this paper, we take a drastic departure from the previously manual ...

The wide application of deep learning technique has raised new security concerns about the training data and test data. In this work, we investigate the model inversion problem under adversarial settings, where the adversary aims at inferring information ...

Neural networks are increasingly employed in safety-critical domains. This has prompted interest in verifying or certifying logically encoded properties of neural networks. Prior work has largely focused on checking existential properties, wherein the ...

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and ...

Recent work proposed the concept of backdoor attacks on deep neural networks (DNNs), where misclassification rules are hidden inside normal models, only to be triggered by very specific inputs. However, these "traditional" backdoors assume a context ...

Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations ...

In Autonomous Vehicles (AVs), one fundamental pillar is perception,which leverages sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving environment. Due to its direct impact on road safety, multiple prior efforts have ...