Sparse Adversarial Attack to Object Detection
Adversarial examples have gained tons of attention in recent years. Many adversarial attacks have been proposed to attack image classifiers, but few work shift attention to object detectors. In this paper, we propose Sparse Adversarial Attack (SAA) which enables adversaries to perform effective evasion attack on detectors with bounded \emph{l$_{0}$} norm perturbation. We select the fragile position of the image and designed evasion loss function for the task. Experiment results on YOLOv4 and FasterRCNN reveal the effectiveness of our method. In addition, our SAA shows great transferability across different detectors in the black-box attack setting. Codes are available at \emph{https://github.com/THUrssq/Tianchi04}.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottom-up Path Augmentation •
Convolution •
Cosine Annealing •
CSPDarknet53 •
CutMix •
Darknet-53 •
DropBlock •
FPN •
Global Average Pooling •
Grid Sensitive •
k-Means Clustering •
Label Smoothing •
Logistic Regression •
Max Pooling •
Mish •
PAFPN •
ReLU •
Residual Connection •
Sigmoid Activation •
Softmax •
Softplus •
Spatial Attention Module •
Spatial Pyramid Pooling •
Tanh Activation •
YOLOv3 •
YOLOv4
