arXiv subjects

Zhe Zhou

Publications and source records attributed to Zhe Zhou.

48 records · Page 3Linked to original sources

arXiv2021

Volcano: Stateless Cache Side-channel Attack by Exploiting Mesh Interconnect

Cache side-channel attacks lead to severe security threats to the settings that a CPU is shared across users, e.g., in the cloud. The existing attacks rely on sensing the micro-architectural state changes made by victims, and this assumption can be invalidated by combining spatial (\eg, Intel CAT) and temporal isolation (\eg, time protection). In this work, we advance the state of cache side-channel attacks by showing stateless cache side-channel attacks that cannot be defeated by both spatial and temporal isolation. This side-channel exploits the timing difference resulted from interconnect congestion. Specifically, to complete cache transactions, for Intel CPUs, cache lines would travel across cores via the CPU mesh interconnect. Nonetheless, the mesh links are shared by all cores, and cache isolation does not segregate the traffic. An attacker can generate interconnect traffic to contend with the victim's on a mesh link, hoping that extra delay will be measured. With the variant delays, the attacker can deduce the memory access pattern of a victim program, and infer its sensitive data. Based on this idea, we implement Volcano and test it against the existing RSA implementations of JDK. We found the RSA private key used by a victim process can be partially recovered. In the end, we propose a few directions for defense and call for the attention of the security community.

cs.CR↗

arXiv2019

Do Not Return Similarity: Face Recovery with Distance

Machine Learning (ML) already has been integrated into all kinds of systems, helping developers to solve problems with even higher accuracy than human beings. However, when integrating ML models into a system, developers may accidentally take not enough care of the outputs of ML models, mainly because of their unfamiliarity with ML and AI, resulting in severe consequences like hurting data owners' privacy. In this work, we focus on understanding the risks of abusing embeddings of ML models, an important and popular way of using ML. To show the consequence, we reveal several kinds of channels in which embeddings are accidentally leaked. As our study shows, a face verification system deployed by a government organization leaking only distance to authentic users allows an attacker to exactly recover the embedding of the verifier's pre-installed photo. Further, as we discovered, with the leaked embedding, attackers can easily recover the input photo with negligible quality losses, indicating devastating consequences to users' privacy. This is achieved with our devised GAN-like structure model, which showed 93.65% success rate on popular face embedding model under black box assumption.

cs.CR↗

arXiv2018

Face Flashing: a Secure Liveness Detection Protocol based on Light Reflections

Face authentication systems are becoming increasingly prevalent, especially with the rapid development of Deep Learning technologies. However, human facial information is easy to be captured and reproduced, which makes face authentication systems vulnerable to various attacks. Liveness detection is an important defense technique to prevent such attacks, but existing solutions did not provide clear and strong security guarantees, especially in terms of time. To overcome these limitations, we propose a new liveness detection protocol called Face Flashing that significantly increases the bar for launching successful attacks on face authentication systems. By randomly flashing well-designed pictures on a screen and analyzing the reflected light, our protocol has leveraged physical characteristics of human faces: reflection processing at the speed of light, unique textual features, and uneven 3D shapes. Cooperating with working mechanism of the screen and digital cameras, our protocol is able to detect subtle traces left by an attacking process. To demonstrate the effectiveness of Face Flashing, we implemented a prototype and performed thorough evaluations with large data set collected from real-world scenarios. The results show that our Timing Verification can effectively detect the time gap between legitimate authentications and malicious cases. Our Face Verification can also differentiate 2D plane from 3D objects accurately. The overall accuracy of our liveness detection system is 98.8\%, and its robustness was evaluated in different scenarios. In the worst case, our system's accuracy decreased to a still-high 97.3\%.

cs.CV↗

arXiv2018

Invisible Mask: Practical Attacks on Face Recognition with Infrared

Accurate face recognition techniques make a series of critical applications possible: policemen could employ it to retrieve criminals' faces from surveillance video streams; cross boarder travelers could pass a face authentication inspection line without the involvement of officers. Nonetheless, when public security heavily relies on such intelligent systems, the designers should deliberately consider the emerging attacks aiming at misleading those systems employing face recognition. We propose a kind of brand new attack against face recognition systems, which is realized by illuminating the subject using infrared according to the adversarial examples worked out by our algorithm, thus face recognition systems can be bypassed or misled while simultaneously the infrared perturbations cannot be observed by raw eyes. Through launching this kind of attack, an attacker not only can dodge surveillance cameras. More importantly, he can impersonate his target victim and pass the face authentication system, if only the victim's photo is acquired by the attacker. Again, the attack is totally unobservable by nearby people, because not only the light is invisible, but also the device we made to launch the attack is small enough. According to our study on a large dataset, attackers have a very high success rate with a over 70\% success rate for finding such an adversarial example that can be implemented by infrared. To the best of our knowledge, our work is the first one to shed light on the severity of threat resulted from infrared adversarial examples against face recognition.

cs.CR↗

arXiv2017

Hyperbolic inverse mean curvature flow

In this paper, we prove the short-time existence of hyperbolic inverse (mean) curvature flow (with or without the specified forcing term) under the assumption that the initial compact smooth hypersurface of $\mathbb{R}^{n+1}$ ($n\geqslant2$) is mean convex and star-shaped. Several interesting examples and some hyperbolic evolution equations for geometric quantities of the evolving hypersurfaces have been shown. Besides, under different assumptions for the initial velocity, we can get the expansion and the convergence results of a hyperbolic inverse mean curvature flow in the plane $\mathbb{R}^2$, whose evolving curves move normally.

math.DG↗

arXiv2017

On the convergence of the time average for skew-product structure and multiple ergodic system

In this paper, for a discontinuous skew-product transformation with the integrable observation function, we obtain uniform ergodic theorem and semi-uniform ergodic theorem. The main assumptions are that discontinuity sets of transformation and observation function are neglected in some measure-theoretical sense. The theorems extend the classical results which have been established for continuous dynamical systems or continuous observation functions. Meanwhile, on the torus $\mathbb{T}^{d}$ with special rotation, we prove the pointwise convergence of multiple ergodic average $\disp \f 1 N \sum_{n=0}^{N-1} f_{1}(R_α^{n}x)f_{2}(R_α^{2n}x)$ in $\mathbb{T}^{d}$.

math.DS↗

arXiv2017

Ergodic behaviour of nonconventional ergodic averages for commuting transformations

Based on T.Tao's result of norm convergence of multiple ergodic averages for commut-ing transformation, we obtain there is a subsequence which converges almost everywhere. Meanwhile, the ergodic behaviour, which the time average is equal to the space average, of diagonal measures is obtained and we give different result according to the classification of transformations. Additionally, on the torus with special rotation. we can not only get the convergence in T.Tao's paper for every point in Td, but also get a beautiful result for ergodic behaviour.

math.DS↗

arXiv2016

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

In this paper, we present that security threats coming with existing GPU memory management strategy are overlooked, which opens a back door for adversaries to freely break the memory isolation: they enable adversaries without any privilege in a computer to recover the raw memory data left by previous processes directly. More importantly, such attacks can work on not only normal multi-user operating systems, but also cloud computing platforms. To demonstrate the seriousness of such attacks, we recovered original data directly from GPU memory residues left by exited commodity applications, including Google Chrome, Adobe Reader, GIMP, Matlab. The results show that, because of the vulnerable memory management strategy, commodity applications in our experiments are all affected.

cs.CR↗

arXiv2014

An Empirical Study on Android for Saving Non-shared Data on Public Storage

With millions of apps that can be downloaded from official or third-party market, Android has become one of the most popular mobile platforms today. These apps help people in all kinds of ways and thus have access to lots of user's data that in general fall into three categories: sensitive data, data to be shared with other apps, and non-sensitive data not to be shared with others. For the first and second type of data, Android has provided very good storage models: an app's private sensitive data are saved to its private folder that can only be access by the app itself, and the data to be shared are saved to public storage (either the external SD card or the emulated SD card area on internal FLASH memory). But for the last type, i.e., an app's non-sensitive and non-shared data, there is a big problem in Android's current storage model which essentially encourages an app to save its non-sensitive data to shared public storage that can be accessed by other apps. At first glance, it seems no problem to do so, as those data are non-sensitive after all, but it implicitly assumes that app developers could correctly identify all sensitive data and prevent all possible information leakage from private-but-non-sensitive data. In this paper, we will demonstrate that this is an invalid assumption with a thorough survey on information leaks of those apps that had followed Android's recommended storage model for non-sensitive data. Our studies showed that highly sensitive information from billions of users can be easily hacked by exploiting the mentioned problematic storage model. Although our empirical studies are based on a limited set of apps, the identified problems are never isolated or accidental bugs of those apps being investigated. On the contrary, the problem is rooted from the vulnerable storage model recommended by Android. To mitigate the threat, we also propose a defense framework.

cs.CR↗

arXiv2014

Adaptive Minimum-Maximum Exclusive Mean Filter for Impulse Noise Removal

Many filters are proposed for impulse noise removal. However, they are hard to keep excellent denoising performance with high computational efficiency. In response to this difficulty, this paper presents a novel fast filter, adaptive minimum-maximum exclusive mean (AMMEM) filter to remove impulse noise. Although the AMMEM filter is a variety of the maximum-minimum exclusive mean (MMEM) filter, however, the AMMEM filter inherits the advantages, and overcomes the drawbacks, compared with the MMEM filter. To increase the various performances of noise removal, the AMMEM filter uses an adaptive size window, introduces two flexible factors, projection factor P and detection factor T, and limits the calculation scope of the AVG. The experimental results show the AMMEM filter makes a significant improvement in terms of noise detection, image restoration, and computational efficiency. Even at noise level as high as 95%, the AMMEM filter still can restore the images with good visual effect.

cs.OH↗

arXiv2014

Your Voice Assistant is Mine: How to Abuse Speakers to Steal Information and Control Your Phone

Previous research about sensor based attacks on Android platform focused mainly on accessing or controlling over sensitive device components, such as camera, microphone and GPS. These approaches get data from sensors directly and need corresponding sensor invoking permissions. This paper presents a novel approach (GVS-Attack) to launch permission bypassing attacks from a zero permission Android application (VoicEmployer) through the speaker. The idea of GVS-Attack utilizes an Android system built-in voice assistant module -- Google Voice Search. Through Android Intent mechanism, VoicEmployer triggers Google Voice Search to the foreground, and then plays prepared audio files (like "call number 1234 5678") in the background. Google Voice Search can recognize this voice command and execute corresponding operations. With ingenious designs, our GVS-Attack can forge SMS/Email, access privacy information, transmit sensitive data and achieve remote control without any permission. Also we found a vulnerability of status checking in Google Search app, which can be utilized by GVS-Attack to dial arbitrary numbers even when the phone is securely locked with password. A prototype of VoicEmployer has been implemented to demonstrate the feasibility of GVS-Attack in real world. In theory, nearly all Android devices equipped with Google Services Framework can be affected by GVS-Attack. This study may inspire application developers and researchers rethink that zero permission doesn't mean safety and the speaker can be treated as a new attack surface.

cs.CR↗

arXiv2014

Acoustic Fingerprinting Revisited: Generate Stable Device ID Stealthy with Inaudible Sound

The popularity of mobile device has made people's lives more convenient, but threatened people's privacy at the same time. As end users are becoming more and more concerned on the protection of their private information, it is even harder to track a specific user using conventional technologies. For example, cookies might be cleared by users regularly. Apple has stopped apps accessing UDIDs, and Android phones use some special permission to protect IMEI code. To address this challenge, some recent studies have worked on tracing smart phones using the hardware features resulted from the imperfect manufacturing process. These works have demonstrated that different devices can be differentiated to each other. However, it still has a long way to go in order to replace cookie and be deployed in real world scenarios, especially in terms of properties like uniqueness, robustness, etc. In this paper, we presented a novel method to generate stable and unique device ID stealthy for smartphones by exploiting the frequency response of the speaker. With carefully selected audio frequencies and special sound wave patterns, we can reduce the impacts of non-linear effects and noises, and keep our feature extraction process un-noticeable to users. The extracted feature is not only very stable for a given smart phone speaker, but also unique to that phone. The feature contains rich information that is equivalent to around 40 bits of entropy, which is enough to identify billions of different smart phones of the same model. We have built a prototype to evaluate our method, and the results show that the generated device ID can be used as a replacement of cookie.

cs.CR↗