Deepfake Detection · · 7 min read

Injection Attacks: The Insurance Fraud Threat Nobody's Talking About

How injection attacks bypass cameras to insert synthetic media directly into insurance claims pipelines. Why most detection tools miss them and how to defend.

Most conversations about deepfake fraud in insurance focus on manipulated photos and AI-generated images. These are real threats. But there’s a more fundamental attack vector that most detection approaches miss entirely: injection attacks.

An injection attack bypasses the camera altogether. Instead of capturing a genuine photo and manipulating it, or generating a photo and submitting it as if it were real, the attacker injects synthetic media directly into the processing pipeline — making the system believe a camera captured something it never did.

This distinction matters because many detection tools are designed to analyze the content of an image for manipulation signs. If the image was never captured by a camera in the first place — if it was injected directly into the data stream — content-level analysis may pass it as genuine.

How Injection Attacks Work

The Camera Bypass

In a normal claims photo submission:

Camera → Photo file → Upload to app → Insurer's system

Each step in this chain adds verifiable signals. The camera embeds EXIF metadata. The phone’s operating system records the file creation. The app logs the upload event.

In an injection attack:

AI-generated image → Injected into pipeline → Insurer's system

The camera step is eliminated. The attacker presents synthetic media directly to the insurer’s system as if it came from a camera. No genuine capture event occurred.

Common Injection Methods

Virtual camera software. Applications like OBS Virtual Camera, ManyCam, or custom solutions allow any image or video to be presented as if it’s a live camera feed. When an insurance mobile app requests camera access for a “live capture” photo, the virtual camera intercepts the request and delivers pre-prepared synthetic content instead.

App-level injection. On rooted Android devices or jailbroken iPhones, the camera API can be hooked — intercepted at the operating system level. When the insurance app calls the camera to capture a photo, the hook delivers a pre-prepared image file instead. The app has no way to distinguish this from a genuine camera capture.

API-level injection. When insurers accept claims via API (common in B2B scenarios, broker submissions, and third-party administrator integrations), there’s no camera involved at all. Media files are submitted as data payloads. The receiving system has no inherent way to verify whether the files were ever captured by a camera.

File metadata spoofing. Even when submitting through standard upload channels, attackers can craft image files with fabricated EXIF metadata — fake camera model, fake GPS coordinates, fake timestamps — that make AI-generated images appear to be genuine camera captures. Simple tools for EXIF manipulation are freely available.

Man-in-the-middle interception. In more sophisticated attacks, genuine photo capture is allowed to occur, but the image is intercepted and replaced in transit between the mobile app and the insurer’s server. The upload metadata shows a genuine capture event, but the actual file content has been swapped.

Why Most Detection Misses This

Content-Focused Detection

The majority of deepfake detection tools analyze image content for signs of AI generation or manipulation:

  • Pixel-level statistical anomalies
  • Frequency domain signatures
  • Visual artifacts

These techniques detect manipulation of existing images and generation of new images. They answer the question: “Does this image contain AI-generated content?”

But a sufficiently advanced injection attack can deliver an image that passes content analysis. If the generated image is high-quality, properly compressed, and doesn’t contain detectable generation artifacts (or the artifacts are obscured by compression), content-level detection may classify it as genuine.

Metadata-Focused Detection

Detection tools that verify EXIF metadata check for consistency: Does the camera model exist? Do timestamps align? Are GPS coordinates plausible?

Injection attacks with spoofed metadata pass these checks. The metadata says “iPhone 16, captured at 34.0522°N 118.2437°W at 14:32 on 2026-01-15” — and none of that is verifiable independently if the attacker has crafted it carefully.

The Missing Layer: Provenance Verification

What most detection approaches lack is the ability to verify that an image was actually captured by a real camera on a real device at the claimed time and place. This requires provenance verification — establishing the chain of custody from the moment of capture through delivery to the insurer.

Defense Against Injection Attacks

Defending against injection attacks requires a different approach from content-level detection. It requires verifying the capture environment, the device, and the delivery chain.

Device Attestation

Modern mobile operating systems provide attestation services that can cryptographically verify:

  • The device is real — not an emulator or virtual machine
  • The operating system is unmodified — not rooted or jailbroken (which would enable API hooks)
  • The app is genuine — not a modified version with injected functionality

Android SafetyNet / Play Integrity API. Google’s device attestation service verifies device integrity, software integrity, and app identity. An insurance app can request attestation before accepting a photo submission, rejecting submissions from compromised devices.

Apple App Attest / DeviceCheck. Apple’s equivalent provides hardware-based attestation that the app is running on a genuine Apple device with an unmodified operating system.

Device attestation doesn’t prove the photo is genuine, but it eliminates the most common injection vectors: virtual cameras on desktops, rooted devices with API hooks, and emulated environments.

Secure Capture

Insurance apps can implement secure capture protocols that verify photos were taken through the app’s own camera interface:

Hardware-level camera binding. Direct access to the camera hardware, bypassing the standard camera API that virtual cameras can intercept. This ensures the image data comes from the physical camera sensor, not a software source.

Cryptographic sealing at capture. Immediately after camera capture, the image is cryptographically signed with a device-specific key and timestamp. Any modification to the image after this point breaks the signature. The insurer verifies the signature on receipt — if it’s valid, the image hasn’t been modified since capture.

Challenge-response capture. The insurer’s server sends a random challenge (a unique code, a specific instruction like “include a blue object in frame”) that the claimant must satisfy during capture. This proves the photo was taken interactively at the moment of submission, not prepared in advance.

Pipeline Integrity

For submissions that don’t go through a mobile app (API submissions, email, web uploads):

Upload chain verification. Track the complete path from source to destination. Log IP addresses, upload timestamps, client information, and transport metadata. Anomalies in the upload chain (e.g., an API submission claiming to be from a mobile device but originating from a datacenter IP) indicate potential injection.

File integrity checking. Compare the internal characteristics of the image file against what would be expected from the claimed capture method. A file claiming to be from an iPhone camera should have specific container format characteristics, compression parameters, and metadata structures. Deviations indicate the file was crafted rather than captured.

Duplicate and template detection. Even sophisticated injection attacks often reuse elements. Cross-referencing submitted media against a database of known injections, common templates, and other submissions can identify patterns.

Multi-Layer Defense

The strongest defense combines all three approaches:

  1. Content analysis — detect AI-generated or manipulated content (catches crude injections and manipulations)
  2. Provenance verification — verify the capture device, method, and chain of custody (catches camera-bypass injections)
  3. Pipeline integrity — verify the submission channel and transport metadata (catches network-level injections)

Each layer catches attacks that others miss. Content analysis alone misses sophisticated injections. Provenance verification alone misses content that was legitimately captured but subsequently manipulated. Pipeline integrity alone misses attacks that use legitimate submission channels.

Implementation Priority

For Mobile Claims Apps

High priority. Mobile apps are the primary submission channel for most personal lines claims. Implementing device attestation and secure capture in your mobile app closes the most common injection vectors.

Practical steps:

  1. Integrate device attestation (SafetyNet/Play Integrity for Android, App Attest for iOS)
  2. Implement secure camera capture with cryptographic sealing
  3. Add challenge-response for high-value claims
  4. Reject submissions from compromised devices or unsupported platforms

For Web Portals

Medium priority. Web-based submissions offer less control over the capture environment, but file integrity checking and upload chain verification provide meaningful protection.

For API/B2B Submissions

Important but different. API submissions have no camera involved by definition. Focus on file integrity checking, source verification, and content-level detection rather than capture verification.

For All Channels

Content-level deepfake detection remains essential. Injection defense complements but doesn’t replace content analysis. Even with secure capture, content-level detection catches manipulation that occurs at the application level or through capture methods that bypass secure capture controls.

The Evolving Threat

Injection attacks are becoming more sophisticated:

  • Virtual camera tools are improving and harder to detect
  • Device attestation bypass techniques exist for determined attackers (though they remain technically challenging)
  • Hybrid attacks combine legitimate capture with post-capture injection — the initial capture is genuine, but the file is replaced before upload

This is an arms race, and the defense must evolve continuously. But implementing multi-layer injection defense now significantly raises the bar for attackers and eliminates the lowest-hanging fruit that currently makes injection trivially easy.

deetech’s multi-layer detection includes injection defense alongside content-level analysis — verifying both what the image shows and how it arrived. Our platform is designed for the full spectrum of insurance fraud attacks, not just content manipulation. Request a demo.

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