19 citations · 34 across the 19 of their papers we have counts for
19 papers
Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation
Kaichao Jiang, Changtao Miao, Baiqi Wu +9
Recent audio-video generators increasingly support joint conditioning on text, images, audio, and video. These capabilities also enable attacks that exploit cross-modal interaction…
ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection
Xiaojing Chen, Xinyu Lu, Changtao Miao +1
AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable AI-generated video detection…
SeClaw: Spec-Driven Security Task Synthesis for Evaluating Autonomous Agents
Hao Cheng, Changtao Miao, Tianle Song +21
Autonomous LLM agents increasingly operate in stateful environments where they access tools, files, memory, and external services. While such capabilities enable complex real-world…
TextFake: Benchmarking AI-Generated Image Detection on Text-Rich Images
Yuning Zhang, Changtao Miao, Mingyu Liao +5
Recent AI-generated image (AIGI) detectors perform well on natural-image benchmarks, but their behavior on text-rich forgeries, such as fabricated screenshots, documents, and news…
GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking
Shupeng Che, Zhiqing Guo, Changtao Miao +2
The rapid evolution of deepfake technology poses an unprecedented threat to the authenticity of Graphics Interchange Format (GIF) imagery, which serves as a representative of short…
DocShield: Towards AI Document Safety via Evidence-Grounded Agentic Reasoning
Fanwei Zeng, Changtao Miao, Jing Huang +8
The rapid progress of generative AI has enabled increasingly realistic text-centric image forgeries, posing major challenges to document safety. Existing forensic methods mainly re…
SAPL: Semantic-Agnostic Prompt Learning in CLIP for Weakly Supervised Image Manipulation Localization
Xinghao Wang, Changtao Miao, Dianmo Sheng +6
Malicious image manipulation threatens public safety and requires efficient localization methods. Existing approaches depend on costly pixel-level annotations which make training e…
GuardTrace-VL: Detecting Unsafe Multimodel Reasoning via Iterative Safety Supervision
Yuxiao Xiang, Junchi Chen, Zhenchao Jin +5
Multimodal large reasoning models (MLRMs) are increasingly deployed for vision-language tasks that produce explicit intermediate rationales. However, reasoning traces can contain u…
Training-Free In-Context Forensic Chain for Image Manipulation Detection and Localization
Rui Chen, Bin Liu, Changtao Miao +5
Advances in image tampering pose serious security threats, underscoring the need for effective image manipulation localization (IML). While supervised IML achieves strong performan…
LaV-CoT: Language-Aware Visual CoT with Multi-Aspect Reward Optimization for Real-World Multilingual VQA
Jing Huang, Zhiya Tan, Shutao Gong +6
As large vision language models (VLMs) advance, their capabilities in multilingual visual question answering (mVQA) have significantly improved. Chain-of-thought (CoT) reasoning ha…
Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning
Ajian Liu, Haocheng Yuan, Xiao Guo +13
PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively. However, isolated training of these…
Inclusion 2024 Global Multimedia Deepfake Detection Challenge: Towards Multi-dimensional Face Forgery Detection
Yi Zhang, Weize Gao, Changtao Miao +10
In this paper, we present the Global Multimedia Deepfake Detection held concurrently with the Inclusion 2024. Our Multimedia Deepfake Detection aims to detect automatic image and a…
Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization
Changtao Miao, Qi Chu, Tao Gong +6
With the advancement of face manipulation technology, forgery images in multi-face scenarios are gradually becoming a more complex and realistic challenge. Despite this, detection…
Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding
Jiazhen Wang, Bin Liu, Changtao Miao +4
AI-synthesized text and images have gained significant attention, particularly due to the widespread dissemination of multi-modal manipulations on the internet, which has resulted…
Multi-spectral Class Center Network for Face Manipulation Detection and Localization
Changtao Miao, Qi Chu, Zhentao Tan +7
As deepfake content proliferates online, advancing face manipulation forensics has become crucial. To combat this emerging threat, previous methods mainly focus on studying how to…
UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection
Wanyi Zhuang, Qi Chu, Zhentao Tan +5
Intra-frame inconsistency has been proved to be effective for the generalization of face forgery detection. However, learning to focus on these inconsistency requires extra pixel-l…
Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning
Wanyi Zhuang, Qi Chu, Haojie Yuan +3
Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative f…
DFGC 2021: A DeepFake Game Competition
Bo Peng, Hongxing Fan, Wei Wang +20
This paper presents a summary of the DFGC 2021 competition. DeepFake technology is developing fast, and realistic face-swaps are increasingly deceiving and hard to detect. At the s…
Towards Generalizable and Robust Face Manipulation Detection via Bag-of-local-feature
Changtao Miao, Qi Chu, Weihai Li +3
Over the past several years, in order to solve the problem of malicious abuse of facial manipulation technology, face manipulation detection technology has obtained considerable at…