DeepTrace
Detection what's true in a world of fakes
Created on 10th November 2024
•
DeepTrace
Detection what's true in a world of fakes
The problem DeepTrace solves
In a world where AI is advancing at an unprecedented rate, the line between reality and deception grows ever thinner. Deepfake technology enables nearly anyone to fabricate convincing videos and audio that threaten personal reputations, corporate security, public trust, and even national security. The implications are severe: with fake news backed by fabricated visuals, high-profile figures misrepresented with false statements, and the erosion of trust in digital information, the stakes have never been higher.
DeepTrace was developed to address this escalating issue. Using advanced deep learning architectures like EfficientNet and Swin Transformer, DeepTrace detects deepfakes with high accuracy by analyzing minute discrepancies in both spatial and temporal data. Unlike typical verification tools, it’s tailored to catch even the most advanced AI-generated manipulations. By integrating with Blockchain, DeepTrace provides a decentralized, tamper-proof solution for tracking content legitimacy, a key use case for blockchain in combating misinformation. It uses Ethereum's transparency and immutability to securely store and verify video authenticity data, addressing the growing issue of media manipulation.
The impact of DeepTrace is transformative. For social media platforms, it serves as an AI-driven safeguard, enabling quick identification of fake content before it spreads. For government and law enforcement, it provides a critical tool to verify digital media, protecting the integrity of information. For organizations, it mitigates reputational risks posed by maliciously altered content.
In essence, DeepTrace doesn’t just detect fakes—it reestablishes trust in digital media. In a world where seeing is no longer believing, DeepTrace brings clarity, security, and peace of mind
Challenges we ran into
Integrating advanced technologies into Deep Trace was far from simple and required overcoming key obstacles to build a reliable deepfake detection tool. Here’s how we tackled these challenges:
Selecting the Optimal Models
With countless AI models to choose from, finding the best fit for our needs was complex. Extensive testing led us to a suitable model, but bias issues emerged. We resolved this by implementing ensemble learning, which combines multiple models to enhance accuracy and reduce bias, making the model more reliable.
Blockchain Integration for Security
Blockchain’s role in ensuring transparency and security was essential, but incorporating it into our system without disrupting functionality was challenging. Through careful adjustments, we preserved blockchain’s core benefits—transparency and immutability—while keeping Deep Trace efficient and user-friendly.
Data Privacy through Federated Learning
Our model trained on general datasets initially, but user-specific fine-tuning was necessary. This raised privacy concerns, as user data needed protection. To address this, we used federated learning, enabling on-device data processing to refine the model without compromising user confidentiality, fully aligning with blockchain’s privacy standards.
Cost Efficiency via Subscription Model
High gas fees associated with blockchain transactions created financial hurdles. To address this, we implemented a subscription-based model, covering gas fees sustainably for our users and enabling continuous access to the service without unpredictable costs.
Tracks Applied (2)
Polygon Track
Polygon
Ethereum Track
ETHIndia
Cheer Project
Cheering for a project means supporting a project you like with as little as 0.0025 ETH. Right now, you can Cheer using ETH on Arbitrum, Optimism and Base.