On the public citation graph
An Enhanced Deep Learning-Based DeepFake Video Detection and Classification System
Inferred edges come from two machine sources: sweeps of open repository
metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is
labelled with the one it came from. Neither is a complete citation index — absence here is not
evidence of absence.
1 of the 44 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1007/s13204-021-02072-3 (notice 2024-01-10).
Dataset join, best-effort; this page does not check the publisher record.
Retraction Watch data via Crossref Labs, used with credit; OpenCitations Index (CC0) and Meta (CC BY 4.0);
dataset doi:10.7910/DVN/WT8VOA (CC BY 4.0).
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated
CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.3390/app11167678openalex-snapshotHigh Performance DeepFake Video Detection on CNN-Based with Attention Target-Specific Regions and Manual Distillation Extraction
inferredcites →https://doi.org/10.3390/e22020249openalex-snapshotA Novel Counterfeit Feature Extraction Technique for Exposing Face-Swap Images Based on Deep Learning and Error Level Analysis
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s12559-025-10465-7openalex-snapshotA Novel Multi-modality Deepfake Detection Using Cross-Model Fusion–Based Hybrid Harris Whale Depth-Wise Separable Dense Convolutional Bi-GRU Deepfake Model
inferred← citeshttps://doi.org/10.1049/ipr2.70152openalex-snapshotDeepFake Detection: Evaluating the Performance of EfficientNetV2‐B2 on Real vs. Fake Image Classification
inferred← citeshttps://doi.org/10.1142/s0218126625503475openalex-snapshotAn Adaptive Convolution Neural Network-Based Multimedia Generative Adversarial Learning Framework for Style Transfer of Movie Videos
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID:
icsacinstitute.org/submit.