Detect deepfakes before they
cause cybersecurity breaches
Take control of deepfake threats with comprehensive detection of synthetic audio, video, and images, deployed securely within your infastructure.
Q1 2025 AI Deepfake Threats Report: Analysis of Risks and Mitigation Strategies
A comprehensive analysis of how synthetic media and deepfakes are reshaping enterprise security in the first quarter of 2025. Based on extensive research and real-world case studies, this report examines emerging threat vectors, quantifies business impact, and provides actionable frameworks for protecting your organization.
On-premise and cloud
Deploy within your infrastructure - from bare metal servers to air-gapped environments. Perfect for organizations requiring complete control over sensitive content analysis while maintaining the highest security standards.
Multi-modal detection
An ensemble of specialized AI models, each optimized for different media types and use cases. Defend against synthetic thread regardless of the source, methods and modality.
Instantaneous threat detection
Identify synthetic media in under 300 milliseconds. Our real-time detection integrates with live streams and communication platforms, allowing immediate response to potential security threats before they can cause harm.
Seamless integration for any security stack
Deploy powerful deepfake protection in minutes, not months. Our straightforward API and flexible deployment options work with your existing security tools and workflows.
Ready to Safeguard Your Organization?
Talk with an expert to see how Detect by Resemble AI integrates to protect your organization against deepfake attacks
Book a DemoFrequently Asked Questions
What is Resemble Detect and how does it help in identifying deepfake audio?
Resemble Detect is a state-of-the-art neural model designed to expose deepfake audio in real-time. It works across all types of media, and against all modern state-of-the-art speech synthesis solutions. By analyzing audio frame-by-frame, it can accurately identify and flag any artificially generated or modified audio content.
Resemble Detect is a state-of-the-art neural model designed to expose deepfake audio in real-time. It works across all types of media, and against all modern state-of-the-art speech synthesis solutions. By analyzing audio frame-by-frame, it can accurately identify and flag any artificially generated or modified audio content.
Can Resemble Detect help protect my intellectual property?
Yes, Resemble AI offers an AI Watermarker to protect your data from being used by unauthorized AI models. By watermarking your data, you can verify if an AI model used your data during its training phase.
How long does Resemble AI's watermarker persist through model training?
Resemble AI’s watermarker is designed to endure throughout the model training process. This means that the watermark, or the unique identifier, remains intact even after the data has undergone various transformations during training.
How does Resemble AIs technology contribute to content creation?
Resemble AI’s generative AI Voices are production-ready and offer a revolutionary way to create content. Whether it’s creating unique real-time conversational agents, translating a voice into multiple languages, or generating thousands of dynamic personalized messages, Resemble AI is altering the content creation landscape. It adds a new level of authenticity and immersion to your content, enhancing audience engagement and overall quality.
How is Resemble Detect trained to identify deepfake audio?
Resemble Detect uses a sophisticated deep neural network that is trained to distinguish real audio from spoofed versions. It analyzes audio frame-by-frame, ensuring any amount of inserted or altered audio can be accurately detected.
How does Resemble AI utilize psychoacoustics in their technology?
Psychoacoustics, the study of human sound perception, plays a significant role in Resemble AI’s technology. By understanding that human sensitivity varies with different frequencies, the technology can embed more information into frequencies we are less sensitive to. Additionally, it utilizes a phenomenon called “auditory masking” where quieter sounds in frequency and time to a louder sound are not perceived, thereby allowing data to be encoded beneath such ‘masking’ sounds.
How does Resemble AI ensure data recovery rate in the presence of various "attacks?"
Resemble AI applies various regularization methods to the model training procedure to resist different types of attacks. Even after applying “attacks” like adding audible noise, time-stretching, time-shifting, re-encoding, and more, nearly 100% data recovery rate can be achieved.
Can I detect if my data was used in training other models with the help of Resemble AI's watermarker?
Absolutely. Because Resemble AI’s watermarker persists through model training, it can be used to identify if your data was used in training other AI models. This feature adds an extra layer of security and allows for better control and protection of your data.