9 accepted papers
AI-generated image detectors achieve high accuracy on in-distribution data but often fail on unseen generators. A key obstacle to understanding this failure is the black-box nature of current detectors: they do not reveal which evidence drives their decisions. We propose \textsc{ForensicConcept}, a …
Audio-visual deepfake detection (AVD) is increasingly important as modern generators can fabricate convincing speech and video. Most current multimodal detectors are small, task-specific models: they work well on curated tests but scale poorly and generalize weakly across domains. We introduce AV-LM…
The rapid development of AI-generated content (AIGC) technology has led to the misuse of highly realistic AI-generated images (AIGI) in spreading misinformation, posing a threat to public information security. Although existing AIGI detection techniques are generally effective, they face two issues:…
The evolution of Diffusion Models has dramatically improved image generation quality making it increasingly difficult to differentiate between real and generated images. This development while impressive also raises significant privacy and security concerns. In response to this we propose a novel La…
In autonomous driving (AD), accurate perception is indispensable to achieving safe and secure driving. Due to its safety-criticality, the security of AD perception has been widely studied. Among different attacks on AD perception, the physical adversarial object evasion attacks are especially severe…
Localization in high-level Autonomous Driving (AD) systems is highly security critical. Recently, researchers found that state-of-the-art Multi-Sensor Fusion (MSF) based localization is vulnerable to GPS spoofing, which can cause road hazards such as driving off road or onto the wrong way. In this w…
Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning. However, they are still criticized for the lack of contextual information and fine-grained details, which in contrast are the merits of traditional grid fe…
Transformer-based architectures have shown great success in image captioning, where object regions are encoded and then attended into the vectorial representations to guide the caption decoding. However, such vectorial representations only contain region-level information without considering the glo…
Recent progress on visual question answering has explored the merits of grid features for vision language tasks. Meanwhile, transformer-based models have shown remarkable performance in various sequence prediction problems. However, the spatial information loss of grid features caused by flattening…