From curated datasets to social-media reality: In-the-wild, multimodal, and generalizable deepfake detection
Source record
naseem-2026-in-the-wild-detection-review· 2026 · Computer Science Review
Evidence tier: C (peer-reviewed-journal) · Verification: verified-abstract (compiler confidence: high)
Bottom line: Review concluding that strong benchmark performance of deepfake detectors does not necessarily carry over to real-world media, because of platform compression, reposting, post-processing, modality mismatch, temporal drift and new generators; calls for deployment-oriented, continually re-benchmarked detection.
Citation
Muhammad Tahir Naseem, et al. (2026). From curated datasets to social-media reality: In-the-wild, multimodal, and generalizable deepfake detection. Computer Science Review, 63, 101081. https://doi.org/10.1016/j.cosrev.2026.101081
Verification entry (identifiers, resolvers, claims with source locations): /data/citations.json#naseem-2026-in-the-wild-detection-review
Study design
| Field | Value |
|---|---|
| Design | Narrative review of deepfake detection from the perspective of in-the-wild robustness, multimodal reasoning and cross-domain generalization (no systematic protocol described in abstract) |
| Modality | review |
| Stimuli | n/a (review) |
| Participants (N) | not recorded |
| N detail | Review; OpenAlex lists Wajid Zaman (Yeungnam University) as last author; full author list not confirmed |
| Topics | detector-generalization, review |
Results
| Metric | Value | Note |
|---|---|---|
| Benchmark-to-wild transfer | curated-benchmark performance does not necessarily translate into reliable real-world performance | qualitative |
| Causes of detector failure | generator shift, platform-induced degradation, modality mismatch, language and speaker variation, contextual diversity | |
| Recommendations | continually updated benchmarks, missing-modality robustness, uncertainty-aware detection, provenance integration |
Limitations
- Narrative review
- No quantitative synthesis in abstract
Cited in
- MR-0001: Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
Verification
| Field | Value |
|---|---|
| Status | verified-abstract |
| Compiler confidence | high |
| Checked on | 2026-10-19 |
| Via | OpenAlex abstract record W7222019517 for doi:10.1016/j.cosrev.2026.101081 |
Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.