4) EfficientNet-B4 [40]: is Based on the EfficientNet architecture [40]. We find that many detectors utilize this architecture as their basic backbone for feature extraction (e.g., SBIs [37], multi-attention [52], etc). Also, as we implement this framework in our benchmark, we can compare the performance of different basic architectures and find the improvement bring by only the architecture.
5) Xception [33]: corresponds to a deepfake detection method based on the XceptionNet model [5] trained on the FaceForensics++ dataset [33]. There are three variants of Xception, namely, Xception-raw, Xceptionc23, and Xception-c40: Xception-raw is trained on raw videos, while Xception-c23 and Xception-c40 are trained on H.264 videos with medium (23) and high degrees (40) of compression, respectively.
6) Capsule [29]: uses capsule structures [34] based on a VGG19 [38] network as the backbone architecture for deepfake classification. This model is originally trained on the FaceForensics++ dataset [33].
7) DSP-FWA [22]: detects deepfake videos using a ResNet-50 [16] to expose the face-warping artifacts introduced by the resizing and interpolation operations in the basic deepfake maker algorithm. This model is trained on self-collected face images. In the original paper, DSP-FWA further improves the FWA algorithm by including a spatial pyramid pooling (SPP) module [15] to better handle the variations in the resolutions of the original target faces. Note that in the DeepfakeBench, we do not adopt the SPP module since we try to use the same architecture (backbone) for each detector so that we can find the actually effective technologies toward deepfake detection. Instead, we use the standard Xception for this detection as other detectors. However, we utilize the multi-scale strategy in the dynamic forgery data generation process to obtain different scale faces blending (the scale parameters we set are  $[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]$ ). Following its paper, we first align the face image to multiple scales and randomly select an aligned image. We visualize blending examples to show that our implementation can achieve similar forgery samples as the original paper (see Sec. A.2).
8) Face X-ray [20]: uses blended artifacts in forgeries to improve generalization ability to detect unseen forgeries. In this work, following the original paper, we train an HRNet [46] both with constructed blended images and fake samples from the considered datasets (FaceForensics++ [33] in our main experiments). Note that the code for this detector is not publicly available, we re-implement it carefully following the instructions and settings in the original paper. We visualize blending examples to show that our implementation can achieve similar forgery samples as the original paper (see Sec. A.2).
9) FFD [6]: applies an attention mechanism to detect and localize manipulation regions. The author proposes two types of attention-based layers, named manipulation appearance model and direct regression, to guide the network to focus on discriminative regions. Meanwhile, three types of loss functions are proposed to supervise the learning progress. In our implementation, we adopt the Xception [5] as the backbone and direct regression as the attention-based layer to train the model.
10) CORE [30]: explicitly constrains the consistency of different representations. Different representations are first captured with different augmentations, and then the cosine distance of the representations is regularized to enhance consistency. This detector utilizes the Xception backbone [5].
11) RECCE [2]: constructs a graph over encoder and decoder features in a multi-scale manner. It further utilizes the reconstruction differences as the forgery traces on the graph output as a guide to the final representation, which is fed into a classifier for forgery detection. End-to-end optimization for reconstruction and classification learning.
12) UCF [50]: introduces a multi-task disentanglement framework to address two main challenges that contribute to the generalization problem in deepfake detection: overfitting to irrelevant features and overfitting to method-specific textures. By uncovering common features, the framework aims to enhance the generalization ability of the model. This detector utilizes the Xception backbone [5]. The code for this detector is not publicly available, we re-implement it carefully following the instructions and settings in the original paper.