Training Data Improvement for Image Forgery Detection using Comprint

• URL: http://arxiv.org/abs/2211.14079v1
• Date: Fri, 25 Nov 2022 12:57:51 GMT
• Title: Training Data Improvement for Image Forgery Detection using Comprint
• Authors: Hannes Mareen, Dante Vanden Bussche, Glenn Van Wallendael, Luisa Verdoliva, and Peter Lambert
• Abstract summary: Comprint enables forgery detection by utilizing JPEG-compression fingerprints. This paper evaluates the impact of the training set on Comprint's performance.
• Score: 15.650121802816878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Manipulated images are a threat to consumers worldwide, when they are used to spread disinformation. Therefore, Comprint enables forgery detection by utilizing JPEG-compression fingerprints. This paper evaluates the impact of the training set on Comprint's performance. Most interestingly, we found that including images compressed with low quality factors during training does not have a significant effect on the accuracy, whereas incorporating recompression boosts the robustness. As such, consumers can use Comprint on their smartphones to verify the authenticity of images.

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