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.
Abstract (translated)
URL
https://arxiv.org/abs/2611.05403
