arXiv Computer Vision
Visual Forensic Detection of Fabricated Bangla News Photocards using Deep Learning
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arXiv Computer Vision
Oct 21
Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection
The paper introduces FADNet, a face authenticity detector that treats forgery detection as a one‑class classification problem, training only on authentic faces. It uses Evidential Deep Learning to measure uncertainty and a pseudo‑forgery generator to sharpen decision boundaries. Experiments on DF40 and ASFD show FADNet surpasses state‑of‑the‑art methods with 96.63% accuracy and 98.83% precision.
arXiv Machine Learning
Jun 26
Adversarial Robustness of AI-Generated Image Detectors in the Real World
arXiv:2410. 01574v4 Announce Type: replace-cross Abstract: The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse.
arXiv Computer Vision
Oct 16
Beyond Generation: Using Multimodal LLMs as Detectors of LLM-Generated Deepfake Facial Images
The paper evaluates whether multimodal large language models (MLLMs) can serve as forensic detectors of deepfake facial images. Six MLLMs—including GPT-4o, GPT-5, GPT-5.2, Gemini 2.5, Gemini 3, and Seed 1.6—were tested on a dataset of 400 images (50 authentic and 350 synthetic) generated by seven leading image models. Results show that several MLLMs achieve high detection accuracy, with GPT-5.2 reaching 97.9% in pairwise comparisons, but the presence of invisible SynthID watermarks can reduce detection performance by about 10% for Gemini.
arXiv AI
Aug 12
Field-Localized Forgery Detection for Digital Identity Documents
arXiv:2605. 09089v2 Announce Type: replace-cross Abstract: Digital onboarding and eKYC systems used by banks, fintech platforms, telecom providers, and other third-party services commonly verify users by comparing an uploaded identity document with a selfie or live facial capture.
arXiv AI
Sep 3
Swin Meets EfficientNet: Lightweight Architectures for GAN-Based Face Forensics
The paper presents lightweight architectures for detecting GAN-generated synthetic faces, comparing a compact Swin Transformer, pre‑trained Swin‑Tiny and Swin‑Small models, and a hybrid EfficientNet‑B0 + Swin Transformer. Using the 140K Real and Fake Faces dataset, the hybrid model achieved 99% accuracy and 99.44% recall on 5,000 test images, outperforming both pure Swin variants and a CNN‑only baseline. The study demonstrates that combining hierarchical CNN features with shifted‑window self‑attention yields an efficient, computationally lightweight detection method.
arXiv AI
Oct 27
A Computer Vision Approach to Visual Fraud Detection in Phishing Websites Using YOLOv8
The paper presents a visual phishing detection system that treats rendered webpages as images, using a YOLOv8 convolutional neural network to classify full-page screenshots as phishing or legitimate. It achieves 92% accuracy, processes a screenshot in about 100 ms, and reduces false positives by 11% after targeted data augmentation. The authors detail dataset creation, augmentation, model architecture, training, and discuss how this visual approach complements existing URL- and content-based defenses.