computer_science2 papersavg year 2026quality 5/5

achieved false detection improvement room

Research gap analysis derived from 2 computer_science papers in our local library.

The gap

The model achieved 81.7% accuracy but some fake images were not detected by the system (120 false negatives out of 500 fake samples), indicating room for improvement in detection sensitivity.; The system achieved detection performance between 81-88%, indicating room for improvement in accuracy and the potential for false negatives in critical security scenarios.

Research trend

Emerging — attention growing, methods still coalescing.

Supporting evidence — 2 representative gaps

  • Deepfake Detection Based Smart Voting System (2026) · doi

    The model achieved 81.7% accuracy but some fake images were not detected by the system (120 false negatives out of 500 fake samples), indicating room for improvement in detection sensitivity.

    Keywords: fake model achieved accuracy images detected system false negatives samples indicating room improvement detection sensitivity
  • Real-Time Edge-Based Burglary Detection and Automated Alerting Using Deep Learning Framework (2026) · doi

    The system achieved detection performance between 81-88%, indicating room for improvement in accuracy and the potential for false negatives in critical security scenarios.

    Keywords: system achieved detection performance indicating room improvement accuracy potential false negatives critical security scenarios

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