62 citations · 116 across the 20 of their papers we have counts for
6 papers · 1 filter
Real is not True: Backdoor Attacks Against Deepfake Detection
Hong Sun, Ziqiang Li, Lei Liu +1
The proliferation of malicious deepfake applications has ignited substantial public apprehension, casting a shadow of doubt upon the integrity of digital media. Despite the develop…
Efficient Trigger Word Insertion
Yueqi Zeng, Ziqiang Li, Pengfei Xia +2
With the boom in the natural language processing (NLP) field these years, backdoor attacks pose immense threats against deep neural network models. However, previous works hardly c…
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks
Ziqiang Li, Pengfei Xia, Hong Sun +3
As the number of parameters in Deep Neural Networks (DNNs) scales, the thirst for training data also increases. To save costs, it has become common for users and enterprises to del…
Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
Ziqiang Li, Hong Sun, Pengfei Xia +4
Recent deep neural networks (DNNs) have came to rely on vast amounts of training data, providing an opportunity for malicious attackers to exploit and contaminate the data to carry…
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks
Ziqiang Li, Hong Sun, Pengfei Xia +6
Poisoning efficiency is crucial in poisoning-based backdoor attacks, as attackers aim to minimize the number of poisoning samples while maximizing attack efficacy. Recent studies h…
Understanding the Error in Evaluating Adversarial Robustness
Pengfei Xia, Ziqiang Li, Hongjing Niu +1
Deep neural networks are easily misled by adversarial examples. Although lots of defense methods are proposed, many of them are demonstrated to lose effectiveness when against prop…