TY - GEN
T1 - Evolution of Detection Performance Throughout the Online Lifespan of Synthetic Images
AU - Karageogiou, Dimitrios
AU - Bammey, Quentin
AU - Porcellini, Valentin
AU - Goupil, Bertrand
AU - Teyssou, Denis
AU - Papadopoulos, Symeon
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Synthetic images disseminated online significantly differ from those used during the training and evaluation of the state-of-the-art detectors. In this work, we analyze the performance of synthetic image detectors as deceptive synthetic images evolve throughout their online lifespan. Our study reveals that, despite advancements in the field, current state-of-the-art detectors struggle to distinguish between synthetic and real images in the wild. Moreover, we show that the time elapsed since the initial online appearance of a synthetic image negatively affects the performance of most detectors. Ultimately, by employing a retrieval-assisted detection approach, we demonstrate the feasibility to maintain initial detection performance throughout the whole online lifespan of an image and enhance the average detection efficacy across several state-of-the-art detectors by 6.7% and 7.8% for balanced accuracy and AUC metrics, respectively.
AB - Synthetic images disseminated online significantly differ from those used during the training and evaluation of the state-of-the-art detectors. In this work, we analyze the performance of synthetic image detectors as deceptive synthetic images evolve throughout their online lifespan. Our study reveals that, despite advancements in the field, current state-of-the-art detectors struggle to distinguish between synthetic and real images in the wild. Moreover, we show that the time elapsed since the initial online appearance of a synthetic image negatively affects the performance of most detectors. Ultimately, by employing a retrieval-assisted detection approach, we demonstrate the feasibility to maintain initial detection performance throughout the whole online lifespan of an image and enhance the average detection efficacy across several state-of-the-art detectors by 6.7% and 7.8% for balanced accuracy and AUC metrics, respectively.
UR - https://www.scopus.com/pages/publications/105007823332
U2 - 10.1007/978-3-031-92648-8_24
DO - 10.1007/978-3-031-92648-8_24
M3 - Conference contribution
AN - SCOPUS:105007823332
SN - 9783031926471
T3 - Lecture Notes in Computer Science
SP - 400
EP - 417
BT - Computer Vision – ECCV 2024 Workshops, Proceedings
A2 - Del Bue, Alessio
A2 - Canton, Cristian
A2 - Pont-Tuset, Jordi
A2 - Tommasi, Tatiana
PB - Springer Science and Business Media Deutschland GmbH
T2 - Workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024
Y2 - 29 September 2024 through 4 October 2024
ER -