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From Spatial CNNs to Multimodal Fusion: A Quantitative Survey of Cross-Dataset Generalization in Deepfake Detection

by Neha Gupta, Rahul Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 122
Year of Publication: 2026
Authors: Neha Gupta, Rahul Kumar
10.5120/ijca00d0437ece9c

Neha Gupta, Rahul Kumar . From Spatial CNNs to Multimodal Fusion: A Quantitative Survey of Cross-Dataset Generalization in Deepfake Detection. International Journal of Computer Applications. 187, 122 ( Jul 2026), 55-62. DOI=10.5120/ijca00d0437ece9c

@article{ 10.5120/ijca00d0437ece9c,
author = { Neha Gupta, Rahul Kumar },
title = { From Spatial CNNs to Multimodal Fusion: A Quantitative Survey of Cross-Dataset Generalization in Deepfake Detection },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2026 },
volume = { 187 },
number = { 122 },
month = { Jul },
year = { 2026 },
issn = { 0975-8887 },
pages = { 55-62 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number122/from-spatial-cnns-to-multimodal-fusion-a-quantitative-survey-of-cross-dataset-generalization-in-deepfake-detection/ },
doi = { 10.5120/ijca00d0437ece9c },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-07-25T01:15:13.857511+05:30
%A Neha Gupta
%A Rahul Kumar
%T From Spatial CNNs to Multimodal Fusion: A Quantitative Survey of Cross-Dataset Generalization in Deepfake Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 122
%P 55-62
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the advent of AI-generated synthetic media, including that produced by generative adversarial networks (GANs), variational autoencoders, and diffusion models, deepfake detection has become a formidable challenge in digital forensics and media integrity. The survey examines 30 representative papers published from 2020 to 2025, and includes every type of spatial detector: CNN-based, frequency-domain analysis, temporal and recurrent networks, vision transformers, contrastive and self-supervised learning and multimodal audio-visual fusion. This survey provides mathematical expressions of several important loss functions, comparison tables of performance across three benchmark datasets (FaceForensics++, Celeb-DF, DFDC), and cross-dataset generalization analysis. The analysis reveals that the gap between the transformer-based and spatial-frequency hybrid detectors is ~24% (relative to the CNN baseline) and highlights open challenges in adversarial robustness, diffusion-model deepfakes, and fairness.

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Index Terms

Computer Science
Information Sciences

Keywords

Deepfake Detection GAN Vision Transformer FaceForensics++ Frequency Domain Contrastive Learning Multimodal Fusion Digital Forensics

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