Detecting AI-edited & fake insurance claim photos
Generative tools make it trivial to fabricate or exaggerate damage in a photo — fake dents, added water damage, invented items. For insurers, screening claim images for AI manipulation is now part of fraud defense.
What to screen for
- Fully AI-generated damage photos (no real event behind them).
- AI-edited real photos — localized inpainting that adds or worsens damage.
- Stripped or inconsistent metadata — missing camera EXIF, edit-software traces, mismatched timestamps.
How it works
An AI-image detector fuses forensic signals — generator fingerprints, EXIF/provenance, edit-history traces, error-level analysis (which highlights locally re-rendered regions), and sensor-noise statistics — into a calibrated risk score. ELA is especially useful for spotting partial edits, where one region of an otherwise real photo has been altered.
Drop it into claims intake
curl -X POST https://api.deef.ai/v1/detect \
-H "Authorization: Bearer $DEEF_KEY" \
--data-binary @claim_photo.jpg
# → {"verdict":"ai_generated","risk":0.88,"remaining":299}
Score each submitted image, auto-pass low-risk claims, and route elevated-risk ones to a human adjuster with the per-signal evidence and a SHA-256-fingerprinted report attached to the file.
Get API credits → Pay-per-use from $1, no subscription. Stopping a single fraudulent payout typically pays for a lot of scans.FAQ
How do insurers detect AI-altered photos?
Add an AI-image detection step at claims intake; route high-risk images to an adjuster with evidence.
Does detection prove fraud?
No — it's calibrated decision support and a documented trail, one input in your fraud workflow.
DEEF.AI provides screening-grade decision support. No detector is 100% accurate; use scores as one signal in adjudication, not as sole proof.
检测 AI 篡改 / 伪造的保险理赔照片
生成式工具让伪造或夸大照片中的损失变得轻而易举——假凹痕、加水渍、凭空多出的物品。对保险公司而言，筛查理赔图像是否被 AI 篡改，已成为反欺诈的一环。
要筛查什么
- 完全 AI 生成的损失照片（背后并无真实事件）。
- AI 编辑过的真实照片——局部重绘，添加或加重损失。
- 被剥离或不一致的元数据——缺相机 EXIF、有编辑软件痕迹、时间戳对不上。
原理
AI 图像检测器把取证信号——生成器指纹、EXIF/溯源、编辑痕迹、误差级别分析（能突出局部被重渲染的区域）、传感器噪声统计——融合成校准过的风险分。ELA 尤其擅长发现局部编辑：一张本来真实的照片里某块区域被改动。
接入理赔受理环节
curl -X POST https://api.deef.ai/v1/detect \
-H "Authorization: Bearer $DEEF_KEY" \
--data-binary @claim_photo.jpg
# → {"verdict":"ai_generated","risk":0.88,"remaining":299}
给每张提交的图像打分：低风险自动通过，偏高风险连同逐信号证据和含 SHA-256 指纹的报告一并转人工核损。
获取 API 次数 → 按次 $1 起，无订阅。拦下一单骗赔，通常就够付很多次扫描了。常见问题
保险公司怎么检测 AI 篡改照片？
在理赔受理加一步 AI 图像检测；高风险图连同证据转核损员。
检测能证明欺诈吗？
不能——它是校准过的决策辅助和留痕证据，是反欺诈流程中的一个输入。
DEEF.AI 提供初筛级决策辅助。没有任何检测器能做到 100% 准确；请把分数作为定责中的一个信号，而非唯一证据。