Text backdoor detection using an interpretable RNN abstract model
Deep neural networks (DNNs) are known to be inherently vulnerable to malicious attacks such as the adversarial attack and the backdoor attack. The former is crafted by adding small perturbations to benign inputs so as to fool a DNN. The latter generally embeds a hidden pattern in a DNN by poisoning...
محفوظ في:
المؤلفون الرئيسيون: | FAN, Ming, SI, Ziliang, XIE, Xiaofei, LIU, Yang, LIU, Ting |
---|---|
التنسيق: | text |
اللغة: | English |
منشور في: |
Institutional Knowledge at Singapore Management University
2021
|
الموضوعات: | |
الوصول للمادة أونلاين: | https://ink.library.smu.edu.sg/sis_research/7118 |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
المؤسسة: | Singapore Management University |
اللغة: | English |
مواد مشابهة
-
Towards interpreting recurrent neural networks through probabilistic abstraction
بواسطة: DONG, Guoliang, وآخرون
منشور في: (2020) -
Stealthy backdoor attack for code models
بواسطة: YANG, Zhou, وآخرون
منشور في: (2024) -
DeepStellar: Model-based quantitative analysis of stateful deep learning systems
بواسطة: DU, Xiaoning, وآخرون
منشور في: (2019) -
Evaluation of backdoor attacks and defenses to deep neural networks
بواسطة: Ooi, Ying Xuan
منشور في: (2024) -
Deepcause: Verifying neural networks with abstraction refinement
بواسطة: NGUYEN HUA GIA PHUC,
منشور في: (2022)