Duckduckgoose

Analyzes doctored media.

text analysis writing

Tool Information

Primary Task	AI content detection
Category	ai-and-machine-learning
Sub Categories	ai-detection
Country	Netherlands

DeepDetector is a deep learning network designed to detect and recognize manipulated faces in images and videos, including deepfakes. This artificial neural network can distinguish between genuine images and computer-generated forgeries by analyzing thousands of real and deepfake images. The technology can extract visible faces in a picture or video, analyze them, and detect deepfake traces with an accuracy rate of approximately 93%. The output of the analysis includes the probability of the input being a deepfake and the Activation Map, which offers an explanation behind the software's decision by substantiating the classification.DeepDetector can not only identify FaceSwaps and other AI-manipulations but also analyze characteristics of AI-generated content to detect synthetic media and AI-generated deepfakes. The tool also offers cloud-based access to customizable APIs to integrate DeepDetector into user workflows, ensuring data protection and privacy through compliance with European laws and regulations regarding data protection, privacy, and responsible AI.DeepDetector can be used for compliance purposes such as KYC, video conferencing, and journalism, as well as in penetration testing. The tool is designed to provide explainable and accurate results, with 10+ deepfake types detected and an image analysis time of 1 second. In summary, DeepDetector is a powerful and reliable tool for detecting deepfakes and other AI-manipulations in images and videos.

DuckDuckGoose AI is a technology company based in the Netherlands, founded in 2020. The company specializes in AI-driven solutions aimed at combating digital manipulation and deepfake threats. Headquartered in Delft, DuckDuckGoose AI focuses on ensuring digital authenticity across various industries that are vulnerable to AI-generated fraud.

Their flagship product, Phocus, offers real-time detection of deepfakes in images, videos, and audio with high accuracy and a very low false positive rate. The company provides cross-format protection that detects synthetic media across all digital formats, including AI-generated text, and offers clear explanations of detected manipulations. Their solutions are designed for seamless integration with existing security infrastructures, ensuring minimal disruption to workflows. DuckDuckGoose AI primarily serves industries that require digital content verification, such as financial institutions and cybersecurity firms, addressing needs in fraud prevention, brand protection, and secure communications.

Pros
Detects manipulated faces Analyzes thousands of images 93% accuracy rate Cloud-based access Customizable APIs Compliance with European laws Data protection and privacy Useful for compliance purposes Useful for video conferencing Useful for journalism Useful for penetration testing Explains results Detects 10+ deepfake types Image analysis in 1 second Trained on representative datasets 93% correct predictions 84.37% precision in fake classifications 92.57% recall in fake predictions Detects synthetic media

Cons
Accuracy rate inconsistency Detection limited to faces Limited deepfake types detection No offline capabilities Requires API integration Reliant on European regulations User interface not mentioned No performance on video content Long term efficacy unproven No information on speed

Frequently Asked Questions

1. What is the main function of DeepDetector?

DeepDetector's main function is detecting deepfakes and other AI-manipulations in images and videos. This involves scanning visible faces in an image or video, analyzing them, and detecting whether or not they have been altered or completely generated by AI.

2. How does DeepDetector detect fake images or videos?

DeepDetector detects fake images or videos in three steps. First, it extracts all visible faces in the picture or video. It then activates its deep learning network to analyze the faces to find deepfake or AI-manipulated traces. Finally, it provides the output of the analysis which includes the probability of the input being a deepfake, as well as an Activation Map that explains the classification.

3. What is the accuracy rate of DeepDetector?

The accuracy rate of DeepDetector is approximately 93%.

4. What is the Activation Map in DeepDetector?

The Activation Map in DeepDetector is a feature that substantiates the AI's decision on whether an input is a deepfake or not. It's a form of explanatory evidence that backs up the software's classification of an image or video.

5. Does DeepDetector offer API integrations?

Yes, DeepDetector offers cloud-based access to customizable APIs for integration into user workflows.

6. What is the image analysis time for DeepDetector?

The image analysis time for DeepDetector is 1 second.

7. What types of deepfakes can DeepDetector detect?

DeepDetector can identify and detect over 10 types of deepfakes. This includes faceswaps, other types of AI-manipulation, and characteristics of AI-generated content like synthetic media.

8. How does DeepDetector ensure data protection and privacy?

DeepDetector ensures data protection and privacy through compliance with European laws and regulations regarding data protection, privacy, and responsible AI. This means all forms of analyses are performed in accordance with these laws and regulations.

9. Can DeepDetector detect AI-generated content?

Yes, DeepDetector is capable of analysing characteristics of AI-generated content in order to detect synthetic media and AI-generated deepfakes.

10. How can DeepDetector be used in KYC compliance?

While it's not specifically detailed, DeepDetector's ability to accurately detect and analyze deepfakes in images and videos can contribute to KYC (Know Your Customer) compliance. The tool could potentially be used to verify the authenticity of customer-submitted visuals, mitigating the risk of identity fraud.

11. What is the purpose of the Activation Map feature in DeepDetector?

The Activation Map feature in DeepDetector offers an explanation behind the software's decision by substantiating the classification. This helps users understand why an image or video was classified as a deepfake.

12. Can DeepDetector be integrated into my existing workflow?

Yes, DeepDetector can be integrated into existing workflows through its customizable APIs, which provide cloud-based access.

13. What is the precision percentage of DeepDetector?

The precision percentage of DeepDetector, which pertains to the percentage of fake classifications that were actually fake, is 84.37%

14. How does DeepDetector substantiate its classifications?

DeepDetector substantiates its classifications through the use of what they call an Activation Map. This map offers correlations between specific features or regions in the input and the model's output, providing a clear explanation for its detection of a deepfake or AI-manipulated image or video.

15. Does DeepDetector comply with European data protection laws?

Yes, DeepDetector complies with European data protection laws. All forms of analyses take place in accordance with European laws and regulations regarding data protection, privacy, and responsible AI.

16. What is the meaning of DeepDetector's 'Explainable Results' feature?

DeepDetector's 'Explainable Results' feature refers to the software's ability to provide clear and understandable reasoning behind its decisions. This is achieved through the activation map, a feature that substantiates the classification of an image or visual as genuine or deepfake.

17. Can DeepDetector detect FaceSwaps and other AI-manipulations?

Yes, DeepDetector can detect FaceSwaps and other AI-manipulations by looking for traces of alterations in existing (camera-made) pictures and videos.

18. What are the steps DeepDetector uses to detect a deepfake?

DeepDetector uses a three-step process to detect deepfakes. Step one is extracting all visible faces in the image or video. Next, it analyzes the faces to find traces of deepfakes. In the final step, it presents the analysis result, including the input's probability of being a deepfake and an Activation Map that substantiates the classification.

19. How does DeepDetector analyze a detected face to find deepfake traces?

After DeepDetector has extracted the faces from a picture or video, it activates its deepfake detection technology. This technology analyzes the faces and investigates them for deepfake traces.