118 citations · 284 across the 16 of their papers we have counts for
19 papers
Microflow: Microarchitectural Causal Observability for Deep Cross-Layer Analysis and Optimization
Saber Ganjisaffar, Chengyu Song, Nael Abu-Ghazaleh
Modern computer architecture relies heavily on simulation to identify bottlenecks and evaluate optimizations. However, existing microarchitectural performance analysis methods are…
MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection
Siyuan Wang, Jianming Zheng, Xuejun Hu +3
Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommoda…
GAMA: Generative Adversarial Multi-Object Scene Attacks
Abhishek Aich, Calvin-Khang Ta, Akash Gupta +4
The majority of methods for crafting adversarial attacks have focused on scenes with a single dominant object (e.g., images from ImageNet). On the other hand, natural scenes includ…
Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks
Abhishek Aich, Shasha Li, Chengyu Song +3
State-of-the-art generative model-based attacks against image classifiers overwhelmingly focus on single-object (i.e., single dominant object) images. Different from such settings,…
Blackbox Attacks via Surrogate Ensemble Search
Zikui Cai, Chengyu Song, Srikanth Krishnamurthy +2
Blackbox adversarial attacks can be categorized into transfer- and query-based attacks. Transfer methods do not require any feedback from the victim model, but provide lower succes…
Zero-Query Transfer Attacks on Context-Aware Object Detectors
Zikui Cai, Shantanu Rane, Alejandro E. Brito +4
Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural…
Context-Aware Transfer Attacks for Object Detection
Zikui Cai, Xinxin Xie, Shasha Li +5
Blackbox transfer attacks for image classifiers have been extensively studied in recent years. In contrast, little progress has been made on transfer attacks for object detectors.…
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
Shasha Li, Abhishek Aich, Shitong Zhu +4
When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied. This could be possible because,…
ADC: Adversarial attacks against object Detection that evade Context consistency checks
Mingjun Yin, Shasha Li, Chengyu Song +3
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, which are slightly perturbed input images which lead DNNs to make wrong predictions. To protec…
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes
Mingjun Yin, Shasha Li, Zikui Cai +4
Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Recent research has shown that checking the intrinsic consistencies in th…
Measurement-driven Security Analysis of Imperceptible Impersonation Attacks
Shasha Li, Karim Khalil, Rameswar Panda +4
The emergence of Internet of Things (IoT) brings about new security challenges at the intersection of cyber and physical spaces. One prime example is the vulnerability of Face Reco…
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
Shasha Li, Shitong Zhu, Sudipta Paul +5
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target ob…
SPECCFI: Mitigating Spectre Attacks using CFI Informed Speculation
Esmaeil Mohammadian Koruyeh, Shirin Haji Amin Shirazi, Khaled N. Khasawneh +2
Spectre attacks and their many subsequent variants are a new vulnerability class affecting modern CPUs. The attacks rely on the ability to misguide speculative execution, generally…
SynFuzz: Efficient Concolic Execution via Branch Condition Synthesis
Wookhyun Han, Md Lutfor Rahman, Yuxuan Chen +3
Concolic execution is a powerful program analysis technique for exploring execution paths in a systematic manner. Compare to random-mutation-based fuzzing, concolic execution is es…
IoTSan: Fortifying the Safety of IoT Systems
Dang Tu Nguyen, Chengyu Song, Zhiyun Qian +3
Today's IoT systems include event-driven smart applications (apps) that interact with sensors and actuators. A problem specific to IoT systems is that buggy apps, unforeseen bad ap…
SPX: Preserving End-to-End Security for Edge Computing
Ketan Bhardwaj, Ming-Wei Shih, Ada Gavrilovska +2
Beyond point solutions, the vision of edge computing is to enable web services to deploy their edge functions in a multi-tenant infrastructure present at the edge of mobile network…
Spectre Returns! Speculation Attacks using the Return Stack Buffer
Esmaeil Mohammadian Koruyeh, Khaled Khasawneh, Chengyu Song +1
The recent Spectre attacks exploit speculative execution, a pervasively used feature of modern microprocessors, to allow the exfiltration of sensitive data across protection bounda…
Adversarial Perturbations Against Real-Time Video Classification Systems
Shasha Li, Ajaya Neupane, Sujoy Paul +4
Recent research has demonstrated the brittleness of machine learning systems to adversarial perturbations. However, the studies have been mostly limited to perturbations on images…
SafeSpec: Banishing the Spectre of a Meltdown with Leakage-Free Speculation
Khaled N. Khasawneh, Esmaeil Mohammadian Koruyeh, Chengyu Song +3
Speculative execution which is used pervasively in modern CPUs can leave side effects in the processor caches and other structures even when the speculated instructions do not comm…