SIH26104: AI-Powered Real-Time Detection and Prevention of Voice Cloning Impersonation Att — SIH 2026 | Wynit

SIH26104: AI-Powered Real-Time Detection and Prevention of Voice Cloning Impersonation Att — SIH 2026 | Wynit

What they'll ask you

13

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Specific latency threshold numbers that define real time?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Dataset of genuine and cloned Indian regional voices to use for training?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact threshold values for triggering risk alerts?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

10 morethe ones a panel gets to if there is time

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for voice authenticity analysis. How would you build that?
Decoded from SIH26104 itself: Performs acoustic, spectral, and prosody analysis using deep learning to detect synthetic speech artifacts. The brief asks for it by name.
The brief asks for real-time risk scoring. How would you build that?
Decoded from SIH26104 itself: Computes a dynamic confidence score indicating the probability of impersonation during live calls. The brief asks for it by name.
The brief asks for alerting and interaction. How would you build that?
Decoded from SIH26104 itself: Sends warnings and recommends secondary verification when impersonation risk crosses set thresholds. The brief asks for it by name.
The brief asks for privacy and compliance. How would you build that?
Decoded from SIH26104 itself: Reduces central audio storage through edge inference, minimal retention, and feature-only logging. The brief asks for it by name.
The brief asks for integration apis and sdks. How would you build that?
Decoded from SIH26104 itself: Provides REST or gRPC endpoints and SDKs to integrate with banking and communication systems. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check whether your model can process audio streams in near real time, whether it handles multiple Indian accents and dialects accurately, and how it integrates via APIs or SDKs.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (686 words)

• Background Recent advancements in generative AI and neural speech synthesis have made high-fidelity voice cloning possible from only a few seconds of recorded audio. Threat actors are exploiting these capabilities to impersonate CXOs,government officials, and trusted individuals in order to initiate fraudulent financial transactions, manipulate employees,or bypass verification procedures in high-risk workflows. Conventional call verification methodsâ€”such as caller ID, manual call-back, and basic voice familiarityâ€”are no longer sufficient to distinguish genuine callers from AI-generated or manipulated voices, especially in high-pressure social engineering scenarios. These attacks are increasingly orchestrated over VoIP, mobile networks, and enterprise collaboration platforms,sometimes combined with leaked personal information to create highly convincing narratives. The absence of automated detection of synthetic or cloned voices in real time significantly increases the likelihood of large-scale financial fraud and reputational damage to institutions that rely heavily on telephonic instructions and approvals. • Problem Statement Current telephony and communication ecosystems lack a robust, AI-driven mechanism to detect and flag voice cloning or synthetic speech impersonation during live calls. Existing solutions rarely perform granular analysis of acoustic artifacts, prosody, and speech generation patterns and typically cannot provide an actionable risk score while the conversation is ongoing.There is a need for an end-to-end security framework that can analyze incoming voice streams in near real time,determine the likelihood that the caller is using a cloned or AI-generated voice, and provide timely alerts and recommendations before sensitive actionsâ€”such as approval of fund transfers or disclosure of confidential informationâ€”are taken. The solution must be privacy-preserving, scalable across telecom and enterprise environments, and support multilingual contexts with diverse Indian accents and dialects. • Proposed Solution Develop an AI-powered, real-time voice integrity verification framework that integrates advanced deep learning,digital signal processing, and contextual analysis to detect AI-generated or manipulated voices. The system should continuously process live or near live audio streams from telephony, VoIP, and collaboration platforms,extract discriminative features, and compute a dynamic impersonation risk score.The framework should expose APIs and SDKs for seamless integration with banking applications, enterprise communication systems, and telecom operator infrastructures, enabling proactive fraud prevention and enhanced cyber resilience in voice channels. • Key Components • Multi-Layer Voice Authenticity Analysis o Acoustic and spectral analysis using deep learning models to detect synthesis artifacts, phase inconsistencies, and spectral signatures indicative of cloned or AI-generated audio. o Prosody and behavioral analysis to model speech rhythm, pitch contours, pauses, and microvariations, differentiating natural human speech from neural TTS outputs. o Cross-session consistency checks comparing ongoing call features against historical genuine samples (where available) to detect anomalies in speaker identity. • Real-Time Risk Scoring Engine o Continuous computation of a confidence/risk score indicating the probability of impersonation or synthetic speech. o Threshold-based alerting logic configurable for different risk scenarios (for example, high-value transaction calls, privileged access approvals). o Contextual enrichment using metadata such as call origin, known contact information, transaction context, and historical fraud indicators. • Alerting and User Interaction Layer o Multi-channel alert mechanisms (UI prompts, SMS/email, in-app notifications) for frontline staff and end users. o Pre-transaction warning prompts recommending secondary verification such as call-back, multifactor authentication, or escalation to supervisors. o Configurable workflows for banks,enterprises, and government agencies to define automated responses when impersonation risk crosses thresholds. • Privacy and Compliance Module o Minimal retention of voice recordings with options for on-device or edge inference to reduce central storage of sensitive audio data. o Support for anonymization or feature-only logging to comply with data protection and privacy requirements. • Platform and Integration APIs o REST/gRPC APIs and SDKs for integration with core banking systems, contact center platforms, enterprise communication tools, and telecom networks. o Support for multiple Indian languages and regional accents through language-agnostic feature extraction and language-specific acoustic models. • Expected Outcomes • Significant reduction in financial fraud and social engineering incidents driven by voice cloning and AIenabled impersonation. • Improved trust and assurance in voice-based communication channels for individuals, financial institutions, enterprises, and government organizations. • Early detection of AI-driven social engineering attacks, enabling proactive containment and incident response. • A reusable security layer for telecom operators and enterprises that strengthens overall cyber resilience and aligns with national cybersecurity objectives.