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Detecting manipulations in digital images is critical for ensuring their authenticity and integrity. This study evaluated the impact of frequency descriptors (DCT/FFT) derived from Error Level Analysis (ELA) maps on manipulation detection performance. Using Random Forest, SVM, XGBoost, and LightGBM on IMD2020, we compared models trained with baseline original image features against those augmented with ELA map features. The results show that ELA-derived features significantly improved the tree-based models. XGBoost and LightGBM yielded the best performance (F1-score ≈ 0.81 validation; XGBoost 0.815 test), demonstrating strong generalization. This study highlights the informative value of combining ELA maps and original image frequency analysis for effective classical machine learning-based detection.

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Effect of Frequency Features of ELA Maps on the Detection Performance of Image Manipulation Based on DCT and FFT Basis Features

  • 会议论文

  • 发表日期：

    2026年

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  • Jarosław Kobiela,
  • Piotr M. Dzierwa

摘要

Detecting manipulations in digital images is critical for ensuring their authenticity and integrity. This study evaluated the impact of frequency descriptors (DCT/FFT) derived from Error Level Analysis (ELA) maps on manipulation detection performance. Using Random Forest, SVM, XGBoost, and LightGBM on IMD2020, we compared models trained with baseline original image features against those augmented with ELA map features. The results show that ELA-derived features significantly improved the tree-based models. XGBoost and LightGBM yielded the best performance (F1-score ≈ 0.81 validation; XGBoost 0.815 test), demonstrating strong generalization. This study highlights the informative value of combining ELA maps and original image frequency analysis for effective classical machine learning-based detection.