1. Perils of AI-enabled voice cloning harms
1. Perils of AI-enabled voice cloning harms
The financial institutions realized that AI is a double-edged sword, potential enough to bring upside benefits in terms of operational efficiency and proven enough to generate harm to the society and swindle the victims’ funds.
AI started playing a vital role in our day-to-day operational life. We feel the gain of reduced labor and our efforts are channelized towards better activities. Equally AI plays a vital role in a fraudsters life by upgrading their fraudulent techniques to scam the victim to siphon their funds.
A clear threat to the financial system is the voice cloning scams, which has become highly prominent with millions of dollars lost due to the AI technology.
It is the responsibility of each financial institution in collaboration with the other enablers like consulting and technology firms to proactively invest and identify a mitigating solution, which should help to prevent the deepfake cloned voice scams.
Institutions demonstrating their responsibility to be proactive in countering the AI driven digital scams will not only help to enhance their brand, reputation as an intelligent forward thinking futuristic firm, but also end up expanding their customer base along with achievable profitable growth.
Few interesting metrics & reports to share :
2. Industry collaboration coupled with our AI innovation lab
Given the darknet markets potential to supply any formatted data at any time to the criminals to execute their large scams and frauds in a seamless manner, the only best way to for financial institutions to counter the fraud rings is via forming a public-private-partnership (PPP) to counter the same.
Realizing this we at Wipro, have collaborated with industry leaders to exchange ideas and independently invested our time and effort to develop sophisticated deep-learning models to combat the AI generated voice scams.
With our missionary statement - ‘the business drives the purpose, and the purpose drives the business’ – our clear strategy is to partner with our clients to develop models to prevent the cloned voice scams on a real-time, via a proof-of-value (PoV) exercise.
As the fraudsters are quite contemporary in nature by introducing new ways of scamming the victims, hence we try to stay relevant by upgrading our AI models by introducing multiple variables to predict the customers behaviors and transaction pattern changes.
Our AI models build with an objective to study the voice data & the behavior patterns like – Change in voice pattern, change in breathing (a sense of urgency), struggling to voice over or talk, unusual voice or fall in voice, to mention a few. Our in-depth domain & consulting knowledge coupled with AI technology acumen helps to think in a futuristic manner to address the contemporary industry challenges.
3. Our approach towards model development and solution build:
We successfully completed the development of an optimized and efficient models to identify the cloned voice addressing both the human and the machine generated voice.
A multi-attention LSTM modl and fine-tuned Vision Transformed (ViT) using our voice sample data.
From our experimentation, both these models demonstrated strong real-time cloned voice identification, achieving up to to 93% accuracy on the training data and 90% accuracy on unseen data.
We trained and validated the models using various open-source datasets, which contributed to these results. However, when applying the models to more realistic, real-world data, the accuracy drops to around 70%.
The efficiency & effectiveness on the model results depends purely on continuous model training with improved data quality viz., diverse set of data sourced from different practical environments, aiming to enhance accuracy to 95%.
By leveraging external, real-world data (rather than open-source datasets), we aim to make the models more robust and adaptable to real-world scenarios.
4. Model Execution Plan: Development & Deployment
The summary of the steps followed so far in the development process are:
a) Building a robust data pipeline – Original data along with cloned data collection
b) Continue to explore avenues to benchmark our work vis-à-vis the industry best standards
c) Continue to explore various models to fine tune the model efficiency and accuracy
d) API endpoints created in AZURE
Step 1: Data Collection & Preparation:
Stage 2: Model Development & Enhancement
Stage 3: Model Validation
Stage 4: Model Deployment
Stage 5: Integration & Accessibility
Stage 6: Monitoring & Continuous Improvement
The above simple development stages helped Wipro to achieve the following:
a) A unique real-time solution to identify the cloned voice scams
b) Address our existing and prospective customers requirements to identify the deepfake voice cloned scams and frauds
We continue to sharpen the solution with our hyper scalar collaboration – Microsoft AZURE and take this developed solution to address the client’s needs on mitigating challenges on voice biometrics.
5. Appendix
[ References, Additional information/reading]
Venkatesh Balasubramaniam
Dr. Gopichand Agnihotram
Joudeep Sarkar
Srilakshmi Subramanian