合成影像偵測器的現況與未來泛化

摘要

隨著不斷推出更新且更優秀的影像生成模型，對合成影像檢測器的需求也隨之增加。在這樣一個充滿活力的領域中，檢測器需要能夠廣泛泛化並對未受控制的變化具有強韌性。本研究的動機來自於這種情況，當探討時間、影像轉換和資料來源對檢測器泛化的作用。在這些實驗中，沒有一個評估的檢測器被認為是通用的，但結果顯示集成可能是一個解決方案。通過野外收集的數據進行的實驗表明，這個任務比大規模數據集所定義的任務更具挑戰性，指出實驗和實際應用之間存在差距。最後，我們觀察到一種競爭均衡效應，即更好的生成器導致更好的檢測器，反之亦然。我們假設這將推動該領域朝著生成器和檢測器之間永無止境的緊密競爭。

English

The continued release of new and better image generation models increases the demand for synthetic image detectors. In such a dynamic field, detectors need to be able to generalize widely and be robust to uncontrolled alterations. The present work is motivated by this setting, when looking at the role of time, image transformations and data sources, for detector generalization. In these experiments, none of the evaluated detectors is found universal, but results indicate an ensemble could be. Experiments on data collected in the wild show this task to be more challenging than the one defined by large-scale datasets, pointing to a gap between experimentation and actual practice. Finally, we observe a race equilibrium effect, where better generators lead to better detectors, and vice versa. We hypothesize this pushes the field towards a perpetually close race between generators and detectors.

合成影像偵測器的現況與未來泛化

Present and Future Generalization of Synthetic Image Detectors

摘要

Summary

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