[Submitted on 19 Oct 2026]
Title:Visual Forensic Detection of Fabricated Bangla News Photocards using Deep Learning
View a PDF of the paper titled Visual Forensic Detection of Fabricated Bangla News Photocards using Deep Learning, by Anindya Kundu and 5 other authors
View PDF HTML (experimental)
Abstract:In Bangladesh, much localized misinformation spreads as photocards: single-image news summaries that imitate the branding of well-known news organizations. Because the misleading message lives in the pixels rather than in machine-readable text, text-based fact-checking tools cannot see it. We recast photocard fake-news detection as visual image forgery detection. We build a dataset of 969 authentic and 1,077 fabricated Bangla photocards scraped from public Facebook pages: authentic cards from the verified pages of seventeen national television broadcasters, fabricated ones from pseudo-broadcaster pages, anonymous profiles, and public groups. Sixteen photometric and morphological transformations per image grow the corpus to 32,736 samples. A fully connected ANN reaches only about 52% accuracy, its loss stuck at ln(2) ~ 0.693, showing that flattening the image destroys the spatial cues forgery detection needs. A compact, fully batch-normalized four-block CNN with 489,954 parameters, about 1,644x smaller than the ANN, is evaluated with leakage-free stratified group five-fold cross-validation, which keeps every augmented version of a card in one fold. Under this protocol the CNN reaches 87.45% +/- 0.53% pooled accuracy from scratch and 88.75% +/- 1.55% when its classifier head is warm-started from the ANN. An ImageNet-pretrained YOLOv8n-cls backbone (1.44M parameters) fine-tuned on the same folds reaches 97.36% +/- 0.42%, marking the headroom above the compact model. We report per-class precision, recall, F1, and pooled confusion matrices, diagnose and fix a one-class collapse caused by an unnormalized classifier head, and give a manual error analysis and bias-variance diagnosis showing that the compact CNNs under-fit.
Comments: 15 pages, 9 figures, 22 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2611.05403 [cs.CV]
(or arXiv:2611.05403v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.05403
arXiv-issued DOI via DataCite
Submission history
From: Anindya Kundu [view email] [v1] Mon, 19 Oct 2026 19:14:13 UTC (5,680 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Visual Forensic Detection of Fabricated Bangla News Photocards using Deep Learning, by Anindya Kundu and 5 other authors
View PDF
HTML (experimental)
TeX Source
view license
Additional Features
Audio Summary
Current browse context:
cs.CV
new | recent | 2026-11
Change to browse by:
cs
References & Citations
SciX
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)