Open Problems

Watermarking & Deepfake Detection

Black-Box and Model-Agnostic Deepfake and Watermark Verification for Closed-Source Generative Models

Barrier to removeOpen
Strong candidate · 4/5 runs4 papers report this100% from 2025+

Generated automatically from the limitations stated in 4 papers (ICML, ICLR, CVPR), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Current watermark verification and deepfake detection methods require white-box access to target model parameters, intermediate layer activations, or gradient information. Because leading generative models and vision-language systems are served exclusively behind closed commercial APIs, internal inspection methods cannot be executed by downstream verifiers or auditors. Consequently, defenders cannot detect deepfakes or verify watermarks when the generating model is proprietary, unknown, or inaccessible. Removing the precondition of internal weight and activation access is necessary for auditing real-world deployed models.

Why it matters

Auditing, watermark verification, and synthetic media provenance tracking become possible on commercial API-only models without requiring provider cooperation or access to internal network states.

Ways to approach it

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  1. 1

    Output-only statistical fingerprinting: Construct an evaluation harness querying proprietary generation APIs (e.g., text and vision-language models) across fixed probe prompts to identify output distributional anomalies or residual statistical watermarks without extracting internal activations. Measure detection AUROC and false positive rate under varying query budgets.

  2. 2

    Surrogate-based black-box transfer verification: Train detection and verification heads on open-source surrogate models and evaluate cross-model transferability against closed target models where weights and internal representations are unknown. Measure transfer attack/detection success rates and calibration error across disparate architectures.

  3. 3

    Decision-based query auditing: Formulate watermark extraction and verification as a black-box query-response game that optimizes test-time prompts to provoke detectable output signatures. Measure verification accuracy, API query cost, and sample efficiency against rate-limited endpoints.

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Why it might fail

Commercial APIs may apply aggressive output filtering or stochastic decoding perturbations that wash out all input-output statistical signatures without access to hidden state logits. Additionally, black-box query budgets might prove prohibitively expensive for low-latency verification pipelines.

Evidence

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

Nearest existing work

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Watermarking & Deepfake Detection

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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.