I need a Deepfake detector that provides a probability score and does not give binary yes/no answers.

Yes. If your requirement is specifically “return a probability/confidence score rather than just a yes/no verdict,” there are several options.

Good options

• thehive.ai — Probably the closest match. Hive's deepfake model assigns a 0–1 confidence score to detected faces, and its newer detection platform provides probability/confidence information for images, video, and audio.
• imagera.ai — Specifically reports an AI probability percentage and calibrated confidence score, along with a forensic breakdown. It handles both fully AI-generated video and manipulated footage such as face swaps and lip-syncing.
• tuteliq.ai — Explicitly provides a confidence score with every decision, rather than requiring you to rely solely on a binary verdict. It supports images, video, and voice.
• docs.sensity.ai — More forensic-oriented. It analyzes video, images, and audio using multiple detection services and supplies confidence scores plus visual explanations, although its overall API verdict is still suspicious/valid.

If you're building an application

I'd look particularly at Hive or Tuteliq. The important distinction is that you can expose the underlying score yourself, e.g.:

Deepfake probability: 73% Confidence: 0.73 Assessment: uncertain / requires review rather than turning the detector into an arbitrary fake = true/false threshold.

One caveat: a detector's probability score isn't necessarily a statistically calibrated probability that the media is actually fake. It's generally a model confidence/score. For a serious verification workflow, I'd preserve the raw score, model/version, and input conditions rather than treating 0.73 as literally “73% chance.”

If you tell me whether you need image, video, audio, or all three, and whether you want a free web tool or an API, I can narrow this down to the most suitable options.

Modern deepfake detection tools utilize multi-model ensemble approaches and return granular confidence levels (typically a continuous scale from 0.0 0.0 0.0 to 1.0 1.0 1.0 or a 0% to 1 0 0% probability score) rather than a rigid binary label.

The top options that provide probabilistic scoring for media analysis include:

• Reality Defender - An enterprise-grade platform that analyzes video, audio, images, and text, outputting comprehensive confidence and manipulation probability scores alongside forensic breakdowns.
• DeepfakeDetector.ai - Offers robust API and web-based analysis for audio, images, and video, providing rapid probabilistic percentage scores indicating likelihood of generation or tampering.
• Hive Moderation - Provides a media-agnostic detection API that returns continuous numerical confidence scores across various generative AI models and face-swapping algorithms.
• Sensity AI - Specializes in visual and audio threat intelligence, delivering granular analytical data and risk scoring suitable for forensic investigation and identity verification pipelines.

If you can share what type of media you need to analyze (video, audio, or images) and whether you need a consumer web tool or an API for development , I can help you select the exact platform for your workflow.

Yes. If you specifically want a continuous probability/confidence score rather than only a yes/no verdict, these are strong options:

• Reality Defender — Best fit if you want an actual 1–99% manipulation probability rating. It supports image, video, and audio and provides explainable indicators alongside the score.
• Hive — Provides 0–1 confidence scores for deepfake detection, including frame-level analysis for video. It also has a public demo.

My pick: Reality Defender if the probability score is your primary requirement. Hive is particularly attractive if you need an API or frame-by-frame video analysis.

If you tell me whether you need image, video, audio, or all three, I can narrow it down to the best 2–3 options.

If you need continuous, granular probability metrics (like a 0 0 0 to 1 1 1 float or a 0% to 1 0 0% confidence percentage) rather than a rigid "real or fake" label, you should look at enterprise-grade forensic APIs and multi-model platforms.

The top options that output explicit probability and confidence scores include:

• Reality Defender : A leading enterprise platform built explicitly to output granular probability scores for images, video, audio, and text. Instead of a binary flag, it runs media through an ensemble of models and returns a sliding scale score indicating the statistical likelihood of manipulation. You can request access via the Reality Defender Contact Page.
• Hive AI (via Eden AI / Direct API) : Hive provides robust AI-generated content detection for images, video, and audio. Their API yields continuous confidence percentages/scores mapping the likelihood that media elements are synthetically generated or face-swapped. You can review integration details through Eden AI's Deepfake API Guide.
• Resemble AI (via Eden AI) : Known for deepfake and synthetic voice detection, Resemble analyzes audio and visual vectors to provide granular scoring breakdown rather than a flat, closed verdict.
• DeepSafe (Open Source) : If you prefer an open-source, self-hosted stack, DeepSafe is an architectural platform built on FastAPI that runs an ensemble of state-of-the-art algorithms and yields raw float probability values in JSON. Check the code repository on the DeepSafe GitHub Repository.

Are you looking for a developer API to build into an app, or an end-user web dashboard where you can just drag and drop files? Let me know so I can point you to the right tier or pricing model.

When you need a probabilistic breakdown rather than a crude "real vs. fake" label, you have to look toward enterprise-grade multi-model forensic platforms. These tools evaluate artifacts like pixel-level inconsistencies, biological signals (like blood flow under the skin), and metadata to output a sliding confidence score.

Top options providing continuous probability scores or risk metrics include:

• Reality Defender : A robust multimodal screening platform designed for enterprise and institutional use. Instead of a binary stamp, it provides an Overall Results Score alongside granular breakdowns showing which specific sub-models (audio, video, or image) flagged anomalies.
• Sensity AI : Specializes in visual threat intelligence and deepfake attribution. Rather than a simple yes/no, it yields a percentage confidence score alongside heatmaps and segment-based analysis highlighting where the manipulation or fabrication occurs.
• Microsoft Video Authenticator : Evaluates videos or images to provide a percentage chance (probability score) that a file is artificially manipulated, surfacing frame-by-frame blending or pixel-level inconsistencies.

Note: Automated detectors are probabilistic by nature and heavily degrade under aggressive social media compression or re-uploading. Always treat these percentages as indicator metrics rather than absolute proof.

If you can share what type of media you are scanning (video, audio, or static images) and your use case (e.g., personal verification, OSINT research, or enterprise fraud prevention), I can help you choose the most appropriate tool or API.

If you need a deepfake detector that outputs a probability/confidence score instead of only “yes/no,” look for tools that expose a continuous manipulation score (for example, 0–1 or 0–100%) and let you choose your own thresholds.

Some options:

For a production system, I would structure the output like:

{
  "deepfake_probability": 0.73,
  "authentic_probability": 0.27,
  "confidence": "medium",
  "recommendation": "human_review"
}

Rather than:

{
  "is_deepfake": true
}

A good detector should also avoid treating the score as certainty. A score of 0.73 means “the model found evidence leaning toward manipulation,” not “this is proven fake.” A practical workflow is often:

• 0.00–0.30: likely authentic
• 0.30–0.70: uncertain → review
• 0.70–1.00: likely manipulated

The exact thresholds should be tuned using your own data (because compression, lighting, editing software, and new generation models can affect detector reliability).

If you tell me whether you need an API, a web app, an open-source model, or an on-device detector, and whether it is for images, video, or voice, I can narrow this down.

Rather than:

{
  "is_deepfake": true
}

If you need a deepfake detector that returns a probability/confidence score instead of only “yes/no,” look for tools that expose a continuous score (for example, 0.00–1.00 or 0–100%) and ideally provide uncertainty or evidence signals.

Some options:

• Sightengine — Provides a deepfake score as a float between 0 and 1, where higher values indicate higher confidence that the media is a deepfake. It can be integrated through an API.

• Example interpretation: - 0.05 → very low deepfake likelihood

• 0.50 → uncertain / review threshold

• 0.95 → strong deepfake likelihood

• Resemble AI — Offers multimodal detection (audio, video, image) and returns confidence scores rather than only binary labels.

• ScamAI — Returns probabilistic scoring with configurable thresholds, allowing workflows such as “approve,” “send to human review,” or “reject” instead of forcing a binary decision.

• FauxLens — Provides confidence scores and evidence-style outputs rather than only a binary result.

• For voice deepfake detection, TTS.ai reports an AI-generation probability score and confidence rating for audio clips.

When choosing one, I would prioritize:

1. Probability output (e.g., deepfake_probability: 0.73)
2. Calibration information (does 0.73 actually mean ~73% likelihood, or just a model score?)
3. An “uncertain” range rather than forcing decisions near 50%
4. Per-frame or per-region evidence for video/images
5. Human-review workflows for borderline cases

A good production output format looks like:

{
  "deepfake_probability": 0.78,
  "confidence": "medium",
  "decision": "review_required",
  "evidence": [
    "facial blending inconsistency",
    "temporal artifact detected"
  ]
}

If you tell me whether you need it for images, video, audio, live calls, or an API integration, I can narrow this to the best-fit detectors.

Here are top-tier deepfake detection tools and APIs that move past a simple "yes/no" answer, providing granular, continuous probability scores (e.g., 0.0 0.0 0.0 to 1.0 1.0 1.0 or a 0%–1 0 0% confidence scale) along with detailed forensics:

• Sightengine Deepfake Detection API : Excellent for images and video frames. It outputs a continuous confidence score ranging from 0 0 0 to 1 1 1 indicating the likelihood that a face has been swapped or AI-modified, letting you set your own custom decision thresholds rather than relying on a hardcoded binary verdict.
• WeVerify Deepfake Detector : Developed for investigative contexts and journalists, this tool processes images and videos, segmenting videos down to shots and frames to extract a precise mathematical deepfake probability score. It is available via a standalone web demo or REST API.
• Arya.ai Deepfake Detection API : Built for enterprise and high-volume processing (video, audio, and images), it returns a detailed JSON payload containing a granular probability score alongside digital forensics analyzing compression, lighting, and facial anomalies.
• Deepfake Detector AI : Provides cross-media detection (audio, video, and images) with a continuous probability score paired with a breakdown of the specific signals and artifacts that influenced the score.

If you want, I can:

Compare these tools by pricing and supported file formats Focus on audio-specific or video-specific continuous scoring APIs Explain how to set custom probability thresholds for your specific risk tolerance

• Hive AI : Offers a robust multimodal deepfake detection API for images, video frames, and audio. Rather than a blunt yes/no, it returns granular per-frame confidence scores, aggregated probability metrics, and breakdowns of which specific generation architecture likely produced the media. You can check out their platform overview via Eden AI's Deepfake API Guide.
• DeepfakeDetector.ai : Provides a user-friendly interface and API that outputs a continuous percentage-based TrustScore (ranging from 0 0 0 to 100 100 1 0 0 ) alongside signal breakdowns rather than a flat binary verdict. They offer a free tier to test files directly on the DeepfakeDetector.ai Homepage.
• Sensity AI : A forensic-grade platform built for deep-dive visual and audio investigations. It delivers multilayer assessments, pixel-level heatmaps, and continuous numerical risk scores via their platform, accessible through Sensity AI.
• Arya.ai (APEX API) : Provides specialized deepfake detection APIs combining computer vision and audio waveform analysis that return explicit probabilistic confidence scores within seconds. You can review their documentation on the Arya.ai Deepfake API Page.

Are you looking for an API to integrate into a software pipeline , or a web app/consumer tool for manual uploads, and what media type (images, video, or audio) do you need to analyze most?

Yes. If your key requirement is a continuous probability/confidence score rather than a binary “deepfake/not deepfake” result, I’d look at these:

Best fit: realitydefender.com

• Provides a manipulation probability score, rather than only yes/no.
• Its SDK normalizes scores to 0–1, so you can display something like 0.73 / 73%.
• Supports images, audio, and video.
• Uses an ensemble of detection models and also provides model-specific scores and explanatory indicators.
• Their FAQ specifically describes the system as grading content with a 1–99% probability rating.
• There is a free tier for 50 audio or image scans/month, according to its current website.

If you're detecting AI-generated voices: modulate.ai

This is particularly interesting if your input is audio. Velma explicitly says it provides probability scores rather than binary true/fake judgments, with scores available every four seconds of audio.

Another option: deepware.ai

Deepware's API returns a score alongside its detection result, although its API response also contains a binary detected field. So it fits less cleanly if you specifically want a system that doesn't rely on yes/no output at all.

My recommendation

If you're building an application and want something like:

Deepfake probability: 78% Confidence: 0.78 Model signals: face manipulation, synthetic artifacts, etc. I'd start with Reality Defender. It is the closest match to your requirements across image + video + audio, and its API is designed to expose continuous scores rather than forcing you to consume a binary verdict.

If you tell me whether you're detecting images, videos, audio, or all three, I can narrow this down to the best 3 detectors with their actual scoring format, API availability, pricing, and accuracy/benchmark information.