| Title | Data Poisoning Attack on Deep Neural Network and Some Defense Methods |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Dang, Tran Khanh, Truong, Phat T. Tran, Tran, Pi To |
| Conference Name | 2020 International Conference on Advanced Computing and Applications (ACOMP) |
| Keywords | Adversarial Machine Learning, AI Poisoning, artificial intelligence, Deep Learning, Human Behavior, information technology, Neural networks, poisoning attack, pubcrawl, Resiliency, Scalability, secure learning, Security in Deep Learning, software engineering, Technological innovation |
| Abstract | In recent years, Artificial Intelligence has disruptively changed information technology and software engineering with a proliferation of technologies and applications based-on it. However, recent researches show that AI models in general and the most greatest invention since sliced bread - Deep Learning models in particular, are vulnerable to being hacked and can be misused for bad purposes. In this paper, we carry out a brief review of data poisoning attack - one of the two recently dangerous emerging attacks - and the state-of-the-art defense methods for this problem. Finally, we discuss current challenges and future developments. |
| DOI | 10.1109/ACOMP50827.2020.00010 |
| Citation Key | dang_data_2020 |
Data Poisoning Attack on Deep Neural Network and Some Defense Methods