Search Results for author:
Found 9 papers, 2 papers with code
VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Human Annotation-Free Pathological Image Classification
To address this issue, we introduce VLM-CPL, a novel approach based on consensus pseudo labels that integrates two noisy label filtering techniques with a semi-supervised learning strategy.
Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Traces
Deepfake videos are becoming increasingly realistic, showing subtle tampering traces on facial areasthat vary between frames.
Recap: Detecting Deepfake Video with Unpredictable Tampered Traces via Recovering Faces and Mapping Recovered Faces
In the recovering stage, the model focuses on randomly masking regions of interest (ROIs) and reconstructing real faces without unpredictable tampered traces, resulting in a relatively good recovery effect for real faces while a poor recovery effect for fake faces.
CTP-Net: Character Texture Perception Network for Document Image Forgery Localization
We design a Character Texture Stream (CTS) based on optical character recognition to capture features of text areas that are essential components of a document image.
Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions
In this paper, we propose a novel SSL method based on Cross Distillation of Multiple Attentions (CDMA) to effectively leverage unlabeled images.
Exploiting Fine-Grained DCT Representations for Hiding Image-Level Messages within JPEG Images
Specifically, we transform the JPEG cover image and hidden secret image into fine-grained DCT representations that compact the frequency and are associated with the inter-block and intra-block correlations.
SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection
Malicious Deepfakes have led to a sharp conflict over distinguishing between genuine and forged faces.
Mover: Mask and Recovery based Facial Part Consistency Aware Method for Deepfake Video Detection
Specifically, given a real face image, we first pretrain a masked autoencoder to learn facial part consistency by dividing faces into three parts and randomly masking ROIs, which are then recovered based on the unmasked facial parts.
Deepfake Detection: A Comprehensive Study from the Reliability Perspective
In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective.
