Deepfake Detection Failure Risk Calculator

Introduction

This calculator estimates the probability that a deepfake video evades automated detection in one stylized screening pipeline. The page is aimed at defensive planning: trust and safety teams, researchers, moderators, policy staff, and security reviewers can use it to compare scenarios, pressure-test assumptions, and explain why some uploads deserve more scrutiny than others. The output is not a courtroom-grade truth score. It is a compact risk estimate built from five practical inputs that often matter in deployment: detector quality, compression quality, attacker skill, frame rate, and duration.

The model is intentionally simple, but that simplicity is useful. It lets you ask plain-language questions such as: If our detector is strong on clean data, how much does heavy re-encoding hurt us? If an adversary is more sophisticated, how much extra review should we expect to need? Do short clips behave differently from longer clips? Everything runs in the browser, so the calculator can be used as a privacy-friendly teaching and planning tool without uploading media.

How to Use

Start with the detector's true positive rate, abbreviated TPR. In plain terms, this is the share of manipulated items your system correctly flags on evaluation data that actually resembles production traffic. If your benchmark is inflated, the calculator will also be optimistic. After that, describe the viewing conditions of the uploaded video: how compressed it is, how skilled the attacker is, how many frames arrive each second, and how long the clip lasts. Those choices determine how much visible forensic signal the calculation assumes remains and how many chances the detector has to notice it.

1. Enter a TPR between 0 and 1. For example, 0.90 means the detector catches 90% of deepfakes on comparable test data.
2. Enter compression quality from 0 to 100, attacker skill from 0 to 10, plus frame rate and duration.
3. Click Calculate to see frames analyzed, effective per-frame detection, failure probability, and the page's display-oriented risk level.

Use the inputs as scenario descriptors, not as exact measurements. Compression quality is a rough stand-in for how much re-encoding, bitrate loss, blur, or upload pipeline degradation makes artifacts harder to spot. Attacker skill is a rough stand-in for how well the creator hides blending errors, temporal inconsistencies, or other clues. Frame rate and duration matter because the model treats each frame as another opportunity to detect synthetic content. That assumption is not perfect in real life, but it helps show the broad direction of change when a clip is short, long, noisy, or clean.

• Detector true positive rate: use a value from representative validation or red-team testing.
• Compression quality: lower values represent heavier degradation and less usable signal.
• Attacker skill: higher values represent more sophisticated evasion and cleaner synthesis.
• Frames per second: higher values create more frame-level opportunities in this model.
• Video duration: longer clips increase total frames and therefore compound the frame-level assumptions.

Formula

The live calculator uses an operational formula that matches the JavaScript on this page. It converts compression quality into a 0 to 1 ratio, applies an attacker-skill penalty, and multiplies both with the baseline TPR to get an effective per-frame detection chance. If we denote $\mathrm{TPR}$ for detector true positive rate, $Q$ for compression quality ratio, $S$ for attacker skill, and $F$ for total frames, then the core failure model preserved from the original page is:

Here, total frames means frame rate multiplied by duration in seconds. The code then maps the failure probability into a display-friendly risk percentage using a logistic curve:

That last step is important: the risk level shown in the result box is not an independent scientific measurement. It is a nonlinear display transform of the modeled failure probability. On this page, the failure probability is the more direct quantity, while the risk level is a friendlier indicator that spreads out the middle of the range. The implementation also clamps impossible values so the effective per-frame detection rate cannot fall below 0 or rise above 1.

Example

Suppose you use the default inputs: detector TPR 0.90, compression quality 80, attacker skill 5, frame rate 30, duration 60 seconds. The code converts quality to 0.80, applies the attacker penalty of 0.50, and gets an effective per-frame detection rate of 0.36. The video contains 1,800 frames. Under the page's frame-independence assumption, that produces a very small overall failure probability because the model treats the detector as getting 1,800 separate chances to notice something. This is a good reminder that the calculator is best for relative comparisons across scenarios rather than as a literal prediction engine.

A quick intuition check helps. If the chance of missing a frame were 0.64, then missing every frame in a long clip would look like ${0.64}^{1800}$, which is essentially zero. That is why long videos can collapse to tiny failure probabilities in a simplified frame model. To see a high output, you usually need a much weaker detector, much lower quality, much higher attacker skill, very short clips, or some combination of all four. In other words, this calculator is most helpful when comparing how risk moves as assumptions worsen or improve, not when declaring certainty about a real upload.

Interpreting the Result

The result panel gives you four numbers. Frames analyzed tells you how much content the model treated as evidence. Effective detection per frame is the post-penalty detection chance after compression and attacker skill are applied. Failure probability is the direct modeled chance that the whole video slips through. Risk level is the logistic display transformation described above. If two scenarios differ only slightly in TPR but greatly in compression quality or attacker skill, the latter two often explain more of the change in the output.

Do not read those bands as policy law. A platform handling election content, fraud, harassment, or biometric abuse may choose far more conservative thresholds than a classroom demo or internal sandbox. The calculator helps structure the discussion; it does not make the final governance decision for you.

Limitations

This calculator deliberately simplifies a messy adversarial problem. First, it assumes a frame-level process with independent opportunities for detection. Real systems often work on clips, shots, or full videos and exploit temporal cues, audio alignment, metadata, or provenance signals. That means the model may understate or overstate risk depending on the design of the actual stack. Second, the page uses attacker skill and compression quality as compact proxies. Those are useful for scenario planning, but they are not direct empirical measurements. Two videos with the same quality score can behave very differently if one preserves face details and another destroys them.

Third, the model says nothing about false positives, moderation workload, or the downstream cost of reviewing flagged content. A detector with excellent deepfake recall can still be operationally poor if it floods analysts with noise. Fourth, the logistic risk transformation is only a visualization aid layered on top of the modeled failure probability. It is not a calibrated threat score trained on outcomes. If you need calibrated decision support, you would normally fit and validate that mapping against historical incidents, human labels, and current attack techniques.

Finally, the model does not include adaptive probing, attacker feedback loops, watermarking, cross-modal verification, ensemble defenses, identity context, or policy exceptions. Use it as a planning aid for comparing conditions and communicating trade-offs. When the stakes are high, combine it with benchmark data, red-team testing, provenance systems, human expertise, and a review workflow that matches your risk tolerance.

Reference Variant Preserved from Earlier Notes

The original page also included a softened conceptual variant that discounts quality less aggressively. It is preserved below because the MathML already existed on the page, but the live calculator result above does not use this exact softened-quality form. Treat it as a reference illustration of the same underlying intuition: weaker signal and stronger adversaries lower effective detection, and repeated misses across frames raise the chance of evasion.