隨著深度學習技術的發展，人工智慧已可生成的高度逼真的圖像，像是影像深偽影片（Deepfake）和生成對抗網絡（Generative Adversarial Network，GAN）都是知名範例，甚至還有以文字生成影片的模型。這些幾可亂真的偽造影像對資訊安全和個人隱私造成威脅，如何分辨真實、電腦生成影像成為熱門研究議題。有別於深度學習以準確性評估模型，本文希望透過影像資料的特性，配合統計理論及資料分析的概念，作為分辨真實及電腦生成人臉影像的依據。
我們認為Deepfake深偽影像和GANs生成圖像存在局部紋理缺陷，前者呈現過度平滑趨勢，後者則有油畫般的紋理扭曲，這些特性可藉由圖像資料的Sobel梯度計算、一階差分等方法偵測出差異。本文將圖像的RGB紅綠藍三原色等九種色彩空間資料代入上述方法，並以這些資料的統計量（平均數、變異數、離群值）為解釋變數，再使用統計學習、機器學習分類模型，判斷影像是否為電腦生成。分析發現本文提議的統計方法之準確性不亞於深度學習模型，而且使用明顯較少的解釋變數，但需選擇適當的資料切割。以PGGAN影像為例，一階差分的切割數較大時，模型準確率約為95%，Sobel則在切割數為64×64時，模型準確率可達99%。另外，模型準確率有明顯的資料依賴性，尤其是GAN資料集，例如：僅在PGGAN有99%準確率，StyleGAN/StyleGAN2等準確率降至20%左右，但若是比對其他真實資料與生成資料時，準確率可達到90%以上。

With the advancement of deep learning technology, artificial intelligence can generate highly realistic images, such as Deepfake videos and Generative Adversari-al Network (GAN). These technologies pose threats to information security and per-sonal privacy, making the differentiation between real and computer-generated im-ages a critical research topic. This paper aims to distinguish between real and com-puter-generated facial images using image characteristics, statistical theories, and data analysis concepts, rather than deep learning accuracy metrics. We consider that Deepfake and GAN-generated images exhibit distinct local texture defects. Deepfake images show excessive smoothness, while GAN images have painting-like distor-tions. These defects can be detected using methods such as Sobel gradient (Liu et al., 2023) and first-order difference (Chen Huishuang, 2023). This study applies these methods to nine color spaces, including RGB, and uses statistical measures (mean, variance, outlier ratio) as explanatory variables in statistical and machine learning classification models.
Our analysis reveals that the proposed statistical classification method achieves accuracy comparable to deep learning models with fewer explanatory variables, and image split is crucial. For PGGAN images, the first-order difference results show that larger split numbers can achieve 95% accuracy, while the Sobel method reaches 99% accuracy with a split number of 64. Additionally, data dependency significantly impacts model accuracy, particularly in GAN datasets. Using the original training dataset yields better results only for PGGAN, whereas StyleGAN/StyleGAN2 data perform worse. However, cross-validated other GAN datasets achieve over 90% ac-curacy.

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