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pro vyhledávání: '"Qiu, Huming"'
In recent years, text-to-image (T2I) generation models have made significant progress in generating high-quality images that align with text descriptions. However, these models also face the risk of unsafe generation, potentially producing harmful co
Externí odkaz:
http://arxiv.org/abs/2411.10329
Deep neural networks (DNNs) are susceptible to backdoor attacks, where malicious functionality is embedded to allow attackers to trigger incorrect classifications. Old-school backdoor attacks use strong trigger features that can easily be learned by
Externí odkaz:
http://arxiv.org/abs/2312.04902
Autor:
Ma, Hua, Wang, Shang, Gao, Yansong, Zhang, Zhi, Qiu, Huming, Xue, Minhui, Abuadbba, Alsharif, Fu, Anmin, Nepal, Surya, Abbott, Derek
All current backdoor attacks on deep learning (DL) models fall under the category of a vertical class backdoor (VCB) -- class-dependent. In VCB attacks, any sample from a class activates the implanted backdoor when the secret trigger is present. Exis
Externí odkaz:
http://arxiv.org/abs/2310.00542
Autor:
Gao, Yansong, Qiu, Huming, Zhang, Zhi, Wang, Binghui, Ma, Hua, Abuadbba, Alsharif, Xue, Minhui, Fu, Anmin, Nepal, Surya
Deep Neural Network (DNN) models are often deployed in resource-sharing clouds as Machine Learning as a Service (MLaaS) to provide inference services.To steal model architectures that are of valuable intellectual properties, a class of attacks has be
Externí odkaz:
http://arxiv.org/abs/2309.11894
The development of unsupervised hashing is advanced by the recent popular contrastive learning paradigm. However, previous contrastive learning-based works have been hampered by (1) insufficient data similarity mining based on global-only image repre
Externí odkaz:
http://arxiv.org/abs/2209.14099
Since Deep Learning (DL) backdoor attacks have been revealed as one of the most insidious adversarial attacks, a number of countermeasures have been developed with certain assumptions defined in their respective threat models. However, the robustness
Externí odkaz:
http://arxiv.org/abs/2204.06273
Autor:
Ma, Hua, Qiu, Huming, Gao, Yansong, Zhang, Zhi, Abuadbba, Alsharif, Xue, Minhui, Fu, Anmin, Jiliang, Zhang, Al-Sarawi, Said, Abbott, Derek
Currently, there is a burgeoning demand for deploying deep learning (DL) models on ubiquitous edge Internet of Things (IoT) devices attributed to their low latency and high privacy preservation. However, DL models are often large in size and require
Externí odkaz:
http://arxiv.org/abs/2108.09187
Autor:
Qiu, Huming, Ma, Hua, Zhang, Zhi, Zheng, Yifeng, Fu, Anmin, Zhou, Pan, Gao, Yansong, Abbott, Derek, Al-Sarawi, Said F.
Though deep neural network models exhibit outstanding performance for various applications, their large model size and extensive floating-point operations render deployment on mobile computing platforms a major challenge, and, in particular, on Inter
Externí odkaz:
http://arxiv.org/abs/2105.03822
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