.png){kind=icon}
返回
Adversarial Attacks on Scene Graph Generation
.png){kind=icon}
PRE
AI
DOI：10.1109/TIFS.2024.3360880.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Scene graph generation (SGG) effectively improves semantic understanding of the visual world. However, the recent interest of researchers focuses on enhancing SGG in non-adversarial settings, which raises our curiosity about the adversarial robustness of SGG models. To bridge this gap, we perform adversarial attacks on two typical SGG tasks, Scene Graph Detection (SGDet) and Scene Graph Classification (SGCls). Specifically, we initially propose a bounding box relabeling method to reconstruct reasonable attack targets for SGCls. It solves the inconsistency between the specified bounding boxes and the scene graphs selected as attack targets. Subsequently, we introduce a two-step weighted attack by removing the predicted objects and relational triples that affect attack performance, which significantly increases the success rate of adversarial attacks on two SGG tasks. Extensive experiments demonstrate the effectiveness of our methods on five popular SGG models and four adversarial attacks.
Keyword:
Task analysis
Object detection
Windows
Visualization
Mirrors
Predictive models
Perturbation methods
Scene graph generation
adversarial attack
bounding box relabeling
two-step weighted attack
期刊
IF:
8
论文数:
5.3K
被引数:
2.3W
机构
引用论文
.png){kind=icon}
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations视觉基因组: 使用众包密集图像注释连接语言和视觉
OAAI
OAAI分享收藏Welding characteristics of aluminum, copper, nickel and aluminum alloy with alumina coating using ultrasonic complex vibration welding equipments铝、铜、镍及铝合金氧化铝涂层超声复合振动焊接特性研究
PREAI
分享收藏
查看更多内容