19 citations · 27 across the 5 of their papers we have counts for
5 papers
Detecting Adversarial Faces Using Only Real Face Self-Perturbations
Qian Wang, Yongqin Xian, Hefei Ling +5
Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when ap…
Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation
Fengfan Zhou, Hefei Ling, Yuxuan Shi +3
Face recognition (FR) models can be easily fooled by adversarial examples, which are crafted by adding imperceptible perturbations on benign face images. The existence of adversari…
DFGC 2021: A DeepFake Game Competition
Bo Peng, Hongxing Fan, Wei Wang +20
This paper presents a summary of the DFGC 2021 competition. DeepFake technology is developing fast, and realistic face-swaps are increasingly deceiving and hard to detect. At the s…
Hands-on Guidance for Distilling Object Detectors
Yangyang Qin, Hefei Ling, Zhenghai He +2
Knowledge distillation can lead to deploy-friendly networks against the plagued computational complexity problem, but previous methods neglect the feature hierarchy in detectors. M…
Selective Convolutional Network: An Efficient Object Detector with Ignoring Background
Hefei Ling, Yangyang Qin, Li Zhang +2
It is well known that attention mechanisms can effectively improve the performance of many CNNs including object detectors. Instead of refining feature maps prevalently, we reduce…