We evaluate our method on three well-known model architectures:, i.e., SSD [ Named Entity Recognition, and Question Answering. Find more details in Table 5. Recall, ROC-AUC, and Average Scanning Overheads for each model. A value of 1 indicates perfect classification, while a value of 0.5 indicates To the best of our knowledge, there is no existing detection methods for object detection models. We evaluate the IoU threshold used to calculate the ASR of inverted triggers. However, a threshold of 0.7 tends to degrade the Different score thresholds are tested when computing the ASR of inverted triggers.

It suffers from performance degradation as the training goes on.

Detecting and segmenting salient objects from given image scenes has received great attention in recent years.

Some previous papers (e.g., MIM [7])claimedthat ensemble with weighted logits (equation (4) in main text) outperforms ensemble with weighted probabilities and weighted combination of loss (equations(3)and(5)in main text). In our experiments, shown in Figure 6b, we observe that weighted combination of surrogate loss functions provide similar or even higher fooling rate compared to weighted probabilities or logits.