Deepfakes have become a growing concern for today’s businesses. In a 2025 Gartner report surveying 302 North American cybersecurity leaders, 62% experienced a deepfake attack. As these technologies evolve and become more accessible, organizations need reliable ways to combat them.
Organizations can take two main approaches to deepfake detection: open source tools and enterprise solutions.
Both use forensic analysis to identify signs of AI generation or manipulation in images, video, and audio. Open source deepfake detection tools are available for free, while enterprise solutions offer managed detection at a cost.
Below, we discuss the main differences between open source and enterprise deepfake detection solutions, how they work, and when to choose each.
Key Takeaways
- Open source deepfake detection offers lower software costs, greater customization, and flexible deployment, but requires significant internal technical resources.
- Enterprise deepfake detection shifts deployment, maintenance, updates, and technical support to the vendor, reducing internal engineering overhead.
- Enterprise detection works best when integrated into workflows such as identity verification, fraud prevention, content moderation, and document review.
- Organizations should regularly benchmark detection tools using authentic, AI-generated, manipulated, compressed, and re-exported files to measure real-world performance.
- TruthScan provides managed AI image, video, and audio detection through its website, Chrome extension, and API, supporting enterprise workflows and high-volume detection needs.
Understanding the Deepfake Detection Landscape
Today’s market provides two primary options for detecting AI-generated and manipulated media: open source and enterprise deepfake detectors.
Open source creators give organizations access to models and code that they can run and customize themselves, while enterprise vendors provide detection with infrastructure, model maintenance, updates, and implementation support.
The most significant difference between the two approaches is cost. Open source tools are usually available for free, while enterprise detection might require paid subscriptions, usage fees, or payment contracts.
Never Worry About AI Fraud Again. TruthScan Can Help You:
- Detect AI generated images, text, voice, and video.
- Avoid major AI driven fraud.
- Protect your most sensitive enterprise assets.
Open source tools also grant organizations more control over the technology. Organizations can modify the software, customize the detection models, and adapt the tools to their specific needs.
Meanwhile, enterprise platforms shift more technical and operational work to the vendor, who handles tasks such as deployment, maintenance, and updates.
Below is a summary of the main differences between enterprise and deepfake detection.
| Open Source Detection | Enterprise Detection | |
| Access | Code and models are publicly available | Vendor provides access to a managed service or platform |
| Cost | Codes and models are free to use, but infrastructure costs money | Usually requires a paid subscription, usage fee, or contract |
| Customization | Teams can modify models and code | Depends on the vendor and available configuration options |
| Deployment | Handled by organization | Hosted or supported by vendor |
| Maintenance and Updates | Managed by organization | Managed by vendor |
| Integration | Developers build and maintain integrations | Vendors may provide APIs and implementation support |
| Technical resources | Requires internal engineering and ML expertise | Reduces the amount of technical work required internally |
| Support | Often relies on documentation and community support | May include dedicated technical and implementation support |
| Scalability | Organization manages infrastructure as demand grows | Vendor typically manages the underlying service infrastructure |
| Model evaluation | Organization must evaluate and monitor performance | Vendor typically handles ongoing model evaluation, while customers should still test performance for their use case |
In short, the choice between open source and enterprise is a question of build vs. buy fraud detection. Open source charges less for the main technology, but requires organizations to commit significant effort to processes like deployment, configuration, integration, maintenance, and performance evaluation.
Meanwhile, enterprise detection offloads the operational work of adoption at the cost of usage fees, subscription fees, or other payment terms.
What Open Source Deepfake Detection Actually Delivers
Open source deepfake detection tools make their code, models, or both available for others to use and modify. Developers can download these tools, run them on their own infrastructure, and adapt them to specific use cases.
The typical open source deepfake detection software is free, making it a solid option for security teams with limited budgets.
In summary, the advantages of open source deepfake detection are:
- Lower software costs: Open source tools typically originate from academic researchers, non-profits, and global developer communities. They share code for free to build a stronger collective defense against the threat of deepfakes.
- More control: Open source tools allow developers to inspect code, modify systems, and customize tools for their needs.
- Flexible deployment: Teams can run the detector within their own infrastructure and control how they integrate it into existing systems.
- Useful for testing: Security and engineering teams can experiment with different detection models before committing to a commercial solution.
However, the cost of accessible software is a lack of hands-on vendor assistance. Typically, open source creators do not provide detailed technical or implementation support.
Organizations using open source tools typically manage the adoption process without help from the code’s original developers, which can slow onboarding.
The processes teams need to handle include:
- Technical setup: Developers must install, configure, and integrate the detection model.
- Infrastructure configuration: Organizations must provide the computing resources needed to run detection at their required volume.
- Maintenance: Teams must monitor the system, fix issues, and keep dependencies up to date.
- Model updates: New deepfake techniques can reduce a model’s effectiveness. Teams may need to find, test, and deploy newer models themselves.
- Performance testing: Organizations must test the detector against their own content to understand how well it performs in their environment.
- Limited support: Many open source projects do not provide dedicated technical or implementation support.
These requirements can make open source detection a good fit for organizations with strong internal engineering and machine learning resources.
However, teams that need a production-ready system without taking on the full development and maintenance burden may prefer enterprise detection solutions.
How Enterprise Deepfake Detection Works
In contrast to open source deepfake detectors, enterprise deepfake detectors provide deepfake detection capabilities tailored to specific enterprise contexts.
A vendor manages the underlying technology and provides access through websites, APIs, browsers, extensions, or other workflows.
Enterprise deepfake detection vendors may provide implementation support, including solution design, workflow integration, and ongoing maintenance and updates. This spares organizations from navigating the adoption process without external guidance.
Integrating Detection Into Enterprise Security Workflows
Enterprise deepfake detection delivers the most value when integrated into existing security and fraud workflows, such as identity verification, fraud prevention, content moderation, and document review.
Organizations can start the integration process by identifying the process that detection can best support. This means narrowing down which areas deepfakes, AI content, and other digitally manipulated files create most risk.
For example, financial institutions might use deepfake detection to block fictitious identities during onboarding. Meanwhile, insurance companies might flag AI-generated vehicle, property, or accident photos.
After identifying a use case, the organization should select the most optimal integration method. Most deepfake detection tools are available through web applications, browser extensions, or APIs.
- Web applications: Teams upload files directly to a web-based platform, which analyzes the content and returns detection results. This option works best for teams with lower detection volumes.
- Browser extensions: Analysts can scan images directly from their browser without downloading and re-uploading them. This option works best for review workflows that are located entirely within a few specific websites.
- APIs: Deepfake API integrations connect detection capabilities directly to an organization’s existing applications, allowing systems to automatically submit files and receive detection results. This option works best for organizations with high detection volumes or workflows that require automated analysis.
Finally, teams should define how they will respond to flagged content. To keep workflows efficient and consistent, different detection scores should trigger clear actions, such as manual review, additional verification, or escalation for further investigation.
Benefits of Enterprise-Grade Deepfake Detection
By shifting most operational burdens to the vendor, enterprise deepfake detection platforms can reduce the work required to deploy and run detection capabilities. Vendors provide hands-on support through the adoption process, freeing teams to focus on core security priorities.
Real-Time Detection & Faster Resolution
Automated detection can analyze content as it enters a workflow. This frees teams from the time-intensive burden of manual review. Analysts can instead focus their time on cases that require more human judgment.
Automation also flags potential risks earlier. This supports quicker decision-making in processes like identity verification, onboarding, fraud investigation, and content review.
Lower Total Cost of Ownership & Engineering Overhead
While open source deepfake detection platforms provide codes and models for free, implementation requires additional costs.
Organizations using open source detectors need to independently develop infrastructure, create integrations, monitor performance, manage updates, and maintain security controls, which can increase overhead.
In contrast, enterprise deepfake detectors provide ready-to-use detection services and production-ready integrations. By reducing the time and resources required to operate detection in-house, they often prove more cost-effective at scale, especially for organizations with limited engineering resources.
Stronger Accuracy, Support & Compliance Coverage
Generally, open source deepfake detectors only provide support through documentation and community channels. Organizations receive no guidance when implementing solutions, testing performance, or aligning tools to compliance requirements.
Meanwhile, enterprise solution vendors can evaluate and update their systems according to AI landscape changes. They also provide technical support, implementation guidance, security documentation, and contractual protections that align with their procurement and compliance requirements.
Best Practices for Evaluating Detection Vendors
Before choosing a provider, teams should ask how the company measures performance, tests new threats, handles customer data, and supports investigations. They should also test the technology with their own datasets to see if its capabilities align with their workflow needs.
Ongoing Benchmarking & Model Updates
Since deepfake technology changes quickly, it is necessary for organizations to ask vendors how often they test and update their models. A good provider should have a process for monitoring new generation methods and evaluating the effectiveness of deepfake detection accuracy against these updates.
Organizations should also conduct their own benchmarking when possible. Teams should test tools against real examples from the media types and workflows the organization handles.
For example, a team that uses deepfake detection during KYC could test the detector with real and fraudulent identity documents. By creating datasets specific to the organization’s workflows, the team can reveal how effectively the tool performs under the conditions they encounter in practice.
It also helps to diversify the spread of media types. Different forms of manipulation and processing can affect detection performance, so a varied test set gives teams a more realistic view of how a tool performs in practice.
A useful evaluation can include:
- Authentic files
- AI-generated files
- Digitally manipulated files
- Compressed and resized files
- Screenshots and re-exports
- Files from different sources and devices
Again, since deepfake technology evolves over time, teams should evaluate detection performance regularly. Relying on an initial test can cause teams to miss new forms of AI-generated or manipulated content as both generation techniques and detection capabilities change.
Security Team Training & Awareness
Deepfake detectors provide the most value when the teams using it understand what its results mean. Organizations can maximize the benefits of deepfake detection technology by investing in training programs that teach security teams how to use detectors, interpret results, and respond appropriately to flagged content.
Training can cover:
- Common signs of deepfake and AI-generated content
- The limits of automated detection
- False positives and false negatives
- Review and escalation procedures
- Documentation requirements
- Changes in emerging AI threats
Teams can also use confirmed fraud cases to improve their internal processes and help analysts recognize recurring patterns.
Aligned Policies & Efficient Review Processes
Detection technologies work best alongside clear policies. When organizations define appropriate actions for different detection results, teams can respond consistently, avoid unnecessary reviews, and ensure suspicious cases receive the appropriate follow-ups.
Typically, organizations can define their policies by answering the following questions:
- Which results require additional actions?
- How should the team respond to flagged content?
- How should the team respond to high-risk cases?
- How should the team respond to false positives?
- What other evidence should analysts evaluate to confirm suspected fraud?
Teams can support detection workflows with thorough recordkeeping. Keeping archives allows teams to review past cases and adapt their processes to new conditions.
How TruthScan Compares to Open Source Detection Tools
While open source deepfake detection tools give organizations flexibility and control, they often require significant internal resources to deploy, maintain, and evaluate.
TruthScan relieves organizations of these operational burdens by providing a managed approach to deepfake detection. It provides AI image, video, and audio detectors designed for enterprise workflows and volumes, with tools and support that help organizations integrate detection into their existing processes.
Organizations can access detection capabilities through three approaches:
- TruthScan website: Security teams can log in and upload suspicious files for analysis.
- Chrome extension: The Chrome extension lets teams right-click images online and scan them without downloading or re-uploading them.
- API: Teams can connect TruthScan to their existing applications and workflows through the standard API.
These options are easy to integrate into existing workflows.
TruthScan is suitable for organizations with high-volume detection needs, limited internal engineering resources, or teams that want a managed alternative to building and maintaining detection infrastructure in-house.
The company also provides implementation support to help teams choose the right integration approach, configure the technology for their workflows, and get up and running efficiently. This ongoing support helps teams address technical needs as they use and scale the technology.
Talk to TruthScan About Enterprise Deepfake Detection
TruthScan can provide AI and digital manipulation detection for enterprise workflows, including identity verification, fraud prevention, and document review. Enterprise plans come with volume discounts and customizable integrations, SLAs, MSAs, and DPAs.
Our team can help you determine which set up best fits your review processes. Contact us to learn more.