Open Problems

Watermarking & Deepfake Detection

Cross-Paradigm Robustness Benchmarking for Deepfake Detectors

Scope to testPartly addressed
Possible candidate · 2/5 runs3 papers report this33% from 2025+

Generated automatically from the limitations stated in 3 papers (ICML, NeurIPS, CVPR), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Current deepfake detectors are developed and validated within narrow manipulation regimes, such as full-frame image synthesis, boundary-blended face swaps, or identity-conditioned video pairs. Because detection models rely on artifacts specific to their target generation family, their performance characteristics across alternative forgery paradigms—such as expression reenactment, localized facial attribute edits, and full-image generation—remain untested. Consequently, practitioners cannot determine whether existing detectors offer any protection outside their specific training domain or if they fail completely when deployed against unmodeled manipulation types.

Why it matters

Unlocks an empirical map of cross-forgery failure modes, establishing which manipulation types transfer across detector paradigms and which require distinct detection techniques.

Ways to approach it

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  1. 1

    Standardized Multi-Paradigm Benchmark: Assemble an evaluation testbed pairing leading detectors (spatial-artifact, frequency-based, and identity-contrastive models) against standardized suites of full-face synthesis, expression swaps (e.g., Face2Face, NeuralTextures), localized attribute edits, and modern diffusion-based face swaps, measuring cross-manipulation AUC-ROC and false-positive degradation.

  2. 2

    Training Regime Sensitivity Audit: Train representative baseline detectors under isolated manipulation assumptions (e.g., identity contrast pairs vs. whole-image blending masks) and measure how detector transferability drops as the testing manipulation diverges in spatial extent and semantic alteration.

  3. 3

    Feature Representation Probing: Extract intermediate detector embeddings across different forgery mechanisms to quantify whether detectors learn domain-general forgery cues or merely overfit to pipeline-specific synthesis artifacts.

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Why it might fail

If concurrent comprehensive benchmark papers evaluate all modern detector families across these exact forgery types, or if standard foundation model features already yield near-perfect zero-shot cross-manipulation generalization across all categories.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

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Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.