VIEWAPP: How to Ensure Data Reliability in Digital Inspections
Digital inspections have already proven their effectiveness — they speed up processes, reduce costs, and make it possible to scale operations without geographical limitations.
However, along with these advantages, a key question inevitably arises: can data collected remotely, without an expert on-site, truly be trusted?
The answer lies not in a single technology, but in how the entire control system is designed.
Reliability begins at the inspection process itself. When a user simply takes photos “freely,” the system is inherently vulnerable. In VIEWAPP, an inspection becomes a guided scenario: the user follows a structured sequence of steps, capturing the object strictly according to a predefined logic. This is not just about convenience — it is a systematic way to eliminate incomplete or distorted data.
But even a well-designed scenario is only part of the solution. It is equally important to understand what happens during the capture process. The system tracks user behavior and analyzes the process: how quickly steps are completed, whether there are attempts to bypass the scenario, and how consistent the sequence of actions is. This makes it possible to detect deviations even before analyzing the actual materials.
Automated photo and video verification plays a key role. Here, multiple layers of control are applied.
- First, file integrity is verified. Checksums (such as MD5 or SHA) are calculated for each image, allowing the system to determine whether a file has been modified after capture. Any attempt at substitution becomes immediately visible.
- Second, image quality is analyzed. The system filters out low-quality photos and detects abnormal characteristics — such as distorted color palettes or artifacts that may indicate editing.
Special attention is given to detecting screen-based photos. This is one of the most common fraud methods — when an image of an object is photographed from another device instead of capturing the real object. Such attempts are identified through characteristic signs: moiré patterns, pixel grids, and screen glow. A neural network trained on thousands of examples automatically detects these cases.
Additionally, the system analyzes the consistency of angles and the overall shooting logic. For example, during a vehicle inspection, it verifies whether the shooting angles are sequential and aligned with the expected inspection structure. This helps identify substituted images or inconsistent photo sets.
Another layer of control is geolocation. GPS data is not just formally recorded but analyzed for anomalies. The system can detect coordinate spoofing, inconsistencies between images and declared location, and attempts to “relocate” the inspection.
A special role is played by anomaly detection. In digital inspections, it is not enough to check compliance with rules — it is crucial to identify deviations. An unusually fast inspection, atypical image parameters, or inconsistencies in data all generate signals that are passed to the expert. These are not hard rejections, but rather areas that require closer attention and additional verification.
At the same time, the system does not replace the expert — it enhances their capabilities. Artificial intelligence acts as a decision-support assistant: it automatically checks data, identifies risks, and presents the expert with structured, pre-filtered information. Instead of spending time on routine review, the expert focuses on cases that truly matter.
A fundamental principle is data immutability. Once uploaded, materials are protected from editing or substitution. This builds trust in inspection results not only within the company, but also among clients and partners.
It is also important to note that such systems are designed with security and regulatory requirements in mind. Data is anonymized, neural networks are trained in secure environments, and algorithms can be audited. This makes digital inspections not only convenient, but also legally robust.
Ultimately, data reliability in digital inspections is not a single feature, but a multi-layered system. Scenario design, process control, data analysis, anomaly detection, and expert involvement all work together. This approach does not simply digitize inspections — it turns them into a reliable source of trustworthy data.