Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 31 for “"adversarial attack"”.
-
Person Re-identification and an Adversarial Attack and Defense for Person Re-identification Networks
… improved significantly. However, latest works in adversarial machine learning have shown the vulnerabilities of DNNs against adversarial examples, which are carefully crafted images that are similar to original/benign images, but can deceive the neural network models. Neural network-based ReID …
-
Person Re-identification And An Adversarial Attack And Defense For Person Re-identification Networks
… improved significantly. However, latest works in adversarial machine learning have shown the vulnerabilities of DNNs against adversarial examples, which are carefully crafted images that are similar to original/benign images, but can deceive the neural network models. Neural network-based ReID …
-
Anomalous Inputs in Deep Learning: a Probabilistic Perspective
… incorrect classifications? This task, known as adversarial attack, involves altering an input to be misclassified while preserving its original semantic content. Both of these tasks are concerned with anomalous inputs to a neural network, but they have so far been addressed by two different …
-
AI-infused security: Robust defense by bridging theory and practice
… three interrelated research thrusts. (1) Adversarial Attack and Defense of Deep Neural Networks: We discover vulnerabilities of deep neural networks in real-world settings and the countermeasures to mitigate the threat. We develop ShapeShifter, the first targeted physical adversarial …
-
Mathematical Optimization Algorithms for Model Compression and Adversarial Learning in Deep Neural Networks
… issue of model size, DNNs are also sensitive to adversarial attacks, a small invisible noise on the input data can fully mislead a DNN. Research on the robustness of DNNs follows two directions in general. The first is to enhance the robustness of DNNs, which increases the degree of difficulty …
-
Exploring Fine-Tuning Techniques for Removing Tamper-Resistant Safeguards for Open-Weight LLMs
… they can be repurposed for malicious tasks via adversarial fine-tuning. In this paper, we evaluate the effectiveness of Tampering Attack Resistance (TAR), a safeguard designed to protect against such adversarial attacks, by exploring its resilience to full-parameter and parameter-efficient …
-
Peer-to-peer network modeling for adversarial proactive cyber defenses
… co-evolutionary algorithms in order to model adversarial attack and defense dynamics in networks. Modeling this behavior is desirable as it allows for network designers to better develop network defense strategies against adaptive cyber attackers. By developing a network simulator that …
-
Towards Out-of-distribution Problem for Reinforcement Learning
… impact of distribution perturbation under the adversarial attack, which validates the sensitivity of deep learning models under even small distribution shifts. To increase the robustness of our system, we propose a detection model in the recommendation system scenario. The second problem we …
-
Bias and Fairness of Evasion Attacks in Image Perturbation
… about protecting privacy of personal images, adversarial attack methods play key roles. These methods are created to protect against the unauthorized usage of personal images. Such methods protect personal privacy by adding some amount of perturbations, otherwise known as "noise", to input …
-
Empowering vision machine perception for robust telehealth applications
… are susceptible to error accumulation and even adversarial attack. We extensively investigate this issue, resulting in the introduction of “persistent TTA” and “reusing of incorrect prediction attack (RIP)” to ensure stability in dynamic testing environments. These contributions drive forward …
-
Adversarial Attacks on Natural Language and Speech Processing Models
… deep learning models are vulnerable to adversarial attacks. A deliberate and specific perturbation of a clean input sample can create an adversarial example, which, when processed by the model, leads to incorrect predictions. Such vulnerabilities can be exploited by malicious adversaries …
-
A Safe and Robust Multi-Agent Motion Planning Framework for Urban Air Mobility
… and detecting system failures or potential adversarial attacks. They must also be prepared to react to the behavior of other agents in the environment expressing a variety of motion planning strategies, which can range from cooperative motion planning, independent motion planning, and even …
-
Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. [Thesis]
… lives, making them a lucrative target for attackers. These devices require suitable security mechanisms that enable robust and effective detection of attacks. Machine learning (ML) and its subdivision Deep Learning (DL) methods offer a promise, but they can be computationally expensive in …
-
Study on Adversarial Robustness of Phishing Email Detection Models
… public phishing/legitimate datasets are lack adversarial email examples which keeps the detection models vulnerable. To address this problem, we developed an augmented phishing/legitimate email dataset, utilizing different adversarial text attack techniques. In this work, the emails that can …
-
ATTACK AND DEFENSE IN SECURITY ANALYTICS
… and prevents the potential threat from adversarial attacks.</p> <p>In the first part, we demonstrate case studies in solving the security problem of categorical classification and time-series abnormal detection. In the proposed framework, we handle the incoming data by utilizing the …
-
Cyber-Physical Attacks and Detection Methods in Water Distribution Systems
… Distribution System (WDS) security and many attackers compromise the critical components of WDS. Cyber-physical attacks (CPAs) are considered one of the biggest challenges that decrease the security factors in WDS by disrupting normal operations and tampering with the critical data of the …
-
A Risk Based Approach to Intelligent Transportation Systems Security
… the world, and the frequency and severity of attacks are on the rise. Healthcare manufacturing, financial services, education, government, and transportation are among the industries that are the most lucrative targets for adversaries. Hacking is not just about companies, organizations, or …
-
Leveraging AI to Combat Misinformation by Empowering Crowds and Evaluating Detectors
… learning models are shown to be vulnerable to adversarial attacks in computer vision and natural language processing domains, the vulnerability of deep sequence embedding-based detectors remains unknown. Thus, we evaluate existing detectors by proposing a novel end-to- end AI algorithm, called …
Page 1 of 2