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Found 7 papers, 4 papers with code
Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective
We design new attack success metrics that can mathematically model the impacts of such design on the TSR system-level attack success, and use them to revisit existing attacks.
LaRE^2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection
To exploit LaRE, we propose an Error-Guided feature REfinement module (EGRE), which can refine the image feature guided by LaRE to enhance the discriminativeness of the feature.
Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack
In this work, we conduct the first measurement study on whether and how effectively the existing designs can lead to system-level effects, especially for the STOP sign-evasion attacks due to their popularity and severity.
SoK: On the Semantic AI Security in Autonomous Driving
In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space.
RSTNet: Captioning With Adaptive Attention on Visual and Non-Visual Words
Then, we build a BERTbased language model to extract language context and propose Adaptive-Attention (AA) module on top of a transformer decoder to adaptively measure the contribution of visual and language cues before making decisions for word prediction.
Dual-Level Collaborative Transformer for Image Captioning
Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning.
Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network
The latter contains a Global Adaptive Controller that can adaptively fuse the global information into the decoder to guide the caption generation.
