Paper: Deepfake Detection with Contrastive Learning in Curved Spaces (ICLR-2024)

Reviewer comment (Novelty):
the paper combines two well-known strategies (contrastive learning on an hypersphere embedding and Busemann prototypes from the Ghadimi et al. Neurips paper). The amount of novelties with this respect is low, and one could expect from such a paper a better justification of the choice of this mixed-curvature space besides ‘the manifold of faces is complicated and non-Euclidean’. Notably, it is not clear which aspects necessary to deepfake detection is captured by the two geometries.

Author response:
Thank you for your remark and recognition.


1. First, we believe that our approach goes beyond a simple combination of two known learning methods. In relation to existing works, different novelties (new loss terms, new ideal prototypes with improved properties) were considered in each space. Additionally, a new method for generating predefined/ideal prototypes was proposed. 

- In hypersphere space (Sec. 3.3), novel terms (the first terms in Eq.3) were incorporated instead of relying solely on the well-known supervised contrastive loss (sCL). This results in improved performance, as demonstrated in Tab.4.  In essence, $L_S$ differs from and outperforms $L_{sCL}$.

- In hyperbolic space (Sec. 3.4), we already clarified the distinctions between our method and that of Atigh et al. (2021) in Appendix (we moved it to the main paper). The main contribution of Atigh et al. (2021) is to integrate a penalized term into the Busemann function to avoid the overconfidence issue. In contrast, we just employ the Busemann function but **without** the penalized term. In our experiments, we find that when using two considered complementary losses, penalizing the distance to the ideal prototype is unnecessary. Moreover, unlike Atigh et al. (2021) that **randomly** samples points from the unit sphere, we use **the predefined evenly distributed points** as ideal prototypes.

- We introduce a novel method to generate evenly distributed vectors (at the end of Sec 3.1) that can be used as both predefined prototypes in sphere space and as ideal prototypes in hyperbolic space. 

2. Second, we have already conducted the experiments to demonstrate the hierarchical relations in deepfake detection datasets. Originally located in the Appendix, we have now integrated them into the main paper. Please refer to Figure 2.
“Left: Inherent hierarchical structure of different manipulations in FF++ represented as a tree. Right: t-SNE projection of embeddings for fake images from the FF++ in the Poincaré ball model. Dotted line circles indicate leaf nodes. CTru effectively captures coarse semantic differences closer to the origin in hyperbolic space (e.g., Reenactment vs. Replacement), with finer-grained distinctions positioned farther away (e.g., Face2Face vs. NeuralTextures).”

We also developed the explanation of using spaces with different curvatures in page 1.

3. Third, we also think that the representation learning aspect may have been overlooked. In Fig. 1, given an image I, we compute $z=f_e(I)$ while $f^S_p$ and $f^H_p$ are only two non-parameter projection operators: $z^S=f^S_p(z) = z/norm(z)$; $z^H=f^H_p(z)=exp_0(z)$ (defined in Sec 3.3 and 3.4). We only have a single vector $z$ which conveys the rich information extracted from image I, **not two**. Our approach involves pushing $z^S$ and $z^H$ towards the predefined/ideal prototypes as part of the learning objective for $z$.

- We further evaluate the performance of feature representations learned with different losses using CIFAR10/100 with ResNet-18 backbone in **Tab. 6 in the revised paper** (considering larger backbones/datasets for future work).  $L_H$ with our predefined prototypes outperforms Atigh et al. (2021). $L_S$ surpasses $L_{sCL}$, and ``Ours'' performs the best, proving the strength of our learned features in image classification tasks.

Dataset  | $L_{CE}$  |  Atigh et al. |  $L_{sCL}$  |  $L_{H}$ + our prototypes | $L_{S}$  | Ours 
---|:---:|:---:|---:|:---:|:---:|:---:
CIFAR10 | 94.9 | 92.3 | 95.0  | 94.1 | 95.2 | 95.4 
CIFAR100 | 76.1 | 65.8 | 76.3  | 68.0 | 76.7 | 77.8

Reviewer comment (Novelty):
All in all, and though the proposed approach seems novel and has merits, it seems to me that the paper would be more suited and impactful in the computer vision community, as far as the novel insight wrt. representation learning are rather limitated.

Author response:
Thank you for your remark and recognition.


1. First, we believe that our approach goes beyond a simple combination of two known learning methods. In relation to existing works, different novelties (new loss terms, new ideal prototypes with improved properties) were considered in each space. Additionally, a new method for generating predefined/ideal prototypes was proposed. 

- In hypersphere space (Sec. 3.3), novel terms (the first terms in Eq.3) were incorporated instead of relying solely on the well-known supervised contrastive loss (sCL). This results in improved performance, as demonstrated in Tab.4.  In essence, $L_S$ differs from and outperforms $L_{sCL}$.

- In hyperbolic space (Sec. 3.4), we already clarified the distinctions between our method and that of Atigh et al. (2021) in Appendix (we moved it to the main paper). The main contribution of Atigh et al. (2021) is to integrate a penalized term into the Busemann function to avoid the overconfidence issue. In contrast, we just employ the Busemann function but **without** the penalized term. In our experiments, we find that when using two considered complementary losses, penalizing the distance to the ideal prototype is unnecessary. Moreover, unlike Atigh et al. (2021) that **randomly** samples points from the unit sphere, we use **the predefined evenly distributed points** as ideal prototypes.

- We introduce a novel method to generate evenly distributed vectors (at the end of Sec 3.1) that can be used as both predefined prototypes in sphere space and as ideal prototypes in hyperbolic space. 

2. Second, we have already conducted the experiments to demonstrate the hierarchical relations in deepfake detection datasets. Originally located in the Appendix, we have now integrated them into the main paper. Please refer to Figure 2.
“Left: Inherent hierarchical structure of different manipulations in FF++ represented as a tree. Right: t-SNE projection of embeddings for fake images from the FF++ in the Poincaré ball model. Dotted line circles indicate leaf nodes. CTru effectively captures coarse semantic differences closer to the origin in hyperbolic space (e.g., Reenactment vs. Replacement), with finer-grained distinctions positioned farther away (e.g., Face2Face vs. NeuralTextures).”

We also developed the explanation of using spaces with different curvatures in page 1.

3. Third, we also think that the representation learning aspect may have been overlooked. In Fig. 1, given an image I, we compute $z=f_e(I)$ while $f^S_p$ and $f^H_p$ are only two non-parameter projection operators: $z^S=f^S_p(z) = z/norm(z)$; $z^H=f^H_p(z)=exp_0(z)$ (defined in Sec 3.3 and 3.4). We only have a single vector $z$ which conveys the rich information extracted from image I, **not two**. Our approach involves pushing $z^S$ and $z^H$ towards the predefined/ideal prototypes as part of the learning objective for $z$.

- We further evaluate the performance of feature representations learned with different losses using CIFAR10/100 with ResNet-18 backbone in **Tab. 6 in the revised paper** (considering larger backbones/datasets for future work).  $L_H$ with our predefined prototypes outperforms Atigh et al. (2021). $L_S$ surpasses $L_{sCL}$, and ``Ours'' performs the best, proving the strength of our learned features in image classification tasks.

Dataset  | $L_{CE}$  |  Atigh et al. |  $L_{sCL}$  |  $L_{H}$ + our prototypes | $L_{S}$  | Ours 
---|:---:|:---:|---:|:---:|:---:|:---:
CIFAR10 | 94.9 | 92.3 | 95.0  | 94.1 | 95.2 | 95.4 
CIFAR100 | 76.1 | 65.8 | 76.3  | 68.0 | 76.7 | 77.8

Reviewer comment (Experiments):
in the experimental section, I did not see the dimensions used fo both embeddings (I may have overlooked). Are they comparable to what is used in other supervised contrastive learning strategies? What is the impact of those dimensions on performances?

Author response:
Thank you for your questions.

As mentioned above, $f^S_p$ and $f^H_p$ are only two **non-parameter** projection operators, and we only have **one embedding** $z$ which conveys the rich information extracted from image I, **not two**. 

As discussed in Sec 3.3, *supervised contrastive learnings are two-stage approaches*. During inference, they discard the projection layer obtained during training and require training additional linear layer(s) (using cross entropy in the original paper, cross-entropy). In contrast, **our method is one-stage**. Once trained either with $L_H$, $L_S$ or both (using predefined ideal prototypes), we can use the network as is (without removing any layer or training additional layer). Classification is **directly** done by comparing the network’s output with predefined prototypes. In our experiments, we achieved similar performance with dimensions of 100 and 200. Similar findings have been observed in previous representation learning works when the output dimension of the projector is relatively low like 128 or 256. We plan to evaluate the performance of our method with higher output dimensions on larger datasets/backbones. However, it is worth noting that the most challenging deepfake datasets are relatively small compared to large-scale datasets comprising of millions samples.

Reviewer comment (Experiments):
in the ablation study, do you keep the total number of dimensions constant (e.g. if S^100 + H^100 is used, do you compare with a hyperspherical embedding with dimension S^200?) I really believe that this question (effectiveness of combination of spherical and hyperbolic geometry) is unsufficiently detailed in the paper.

Author response:
Thank you for your questions.

The dimension of $z^S$ and $z^H$ is identical to that of $z$, **yet at no point during training/inference do we combine the two vectors** $z^S$ and $z^H$. Our approach involves pushing $z^S$ and $z^H$ towards the predefined/ideal prototypes as part of the learning objective for $z$. As mentioned above, in our experiments, we achieved similar performance with relatively low dimensions of 100 and 200. We further demonstrated the effectiveness of our method in image classification tasks in Tab. 6.

We are eager to continue the discussion. Have all your concerns been addressed in our rebuttal, or are there any remaining comments you would like us to consider?

Reviewer comment (Experiments):
The authors should provide more case studies in the main manuscript, including new features in Section 3 and facial features after clustering using contrastive loss.

Author response:
Thank you for your suggestion. Indeed, we have already conducted the experiments to visualize the features after clustering using contrastive loss. **Originally located in the Appendix, we have now integrated them into the main paper.** Please refer to Figure 2.
``Left: Inherent hierarchical structure of different manipulations in FF++ represented as a tree. Right: t-SNE projection of embeddings for fake images from the FF++ in the Poincaré ball model. Dotted line circles indicate leaf nodes. CTru effectively captures coarse semantic differences closer to the origin in hyperbolic space (e.g., Reenactment vs. Replacement), with finer-grained distinctions positioned farther away (e.g., Face2Face vs. NeuralTextures).''

Reviewer comment (Evaluation):
More analysis on efficiency should be added, such as overall training time, parameters, and convergence steps.

Author response:
Thank you for your remarks. Some implementation details (number of training epochs, scheduler, inference time) were already provided but some were in Appendix. We synthesized them in the main paper.

We employ the AdamW optimizer with an initial learning rate of $0.002$ and no weight decay.
The model is trained for 200 epochs using the cosine scheduler without restart, preceded by a linear warmup of 20 epochs. We use a batch size of 64. Images are resized to $299 \times 299$ before being fed into the model.
As many other recent detectors like SBI, SLADD, we use EffNetb4 with *19M parameters* as backbone by default. Training is conducted on two NVIDIA V100 GPUs and requires approximately *16 hours* while during inference, CTru achieves a frame rate of 74fps on a PC with RTX3060.

Reviewer comment (Experiments):
It is suggested that the authors consider applying this method to other well-known backbones, such as Xception.

Author response:
Thank you for your suggestion. Xception has been widely utilized in deepfake detection. However, more recent studies, such as SBI, SLADD, have indicated that among CNN backbones, EffNet-b4 has demonstrated superior performance to Xception. In response to your recommendation, we conducted an evaluation of our algorithm using ResNet-50 and Xception. **As shown in Table 5 in the revised version**, our findings align with prior research. Currently, we are exploring Transformer backbones, but please note that they come with a higher parameter count and require pretraining on larger datasets than deepfake datasets.

Reviewer comment (Novelty):
The reason to combine both negative and positive curvature representation spaces in deepfake detection is not insightful. This makes the paper merely a combination of existing techniques.

Author response:
Thank you for your remarks.

1. First, we believe that our approach goes beyond a simple combination of two known learning methods. In relation to existing works, different novelties (new loss terms, new ideal prototypes with improved properties) were considered in each space. Additionally, a new method for generating predefined/ideal prototypes was proposed. 

- In hypersphere space (Sec. 3.3), novel terms (the first terms in Eq.3) were incorporated instead of relying solely on the well-known supervised contrastive loss (sCL). This results in improved performance, as demonstrated in Tab.4.  In essence, $L_S$ differs from and outperforms $L_{sCL}$.

- In hyperbolic space (Sec. 3.4), we already clarified the distinctions between our method and that of Atigh et al. (2021) in Appendix (we moved it to the main paper). The main contribution of Atigh et al. (2021) is to integrate a penalized term into the Busemann function to avoid the overconfidence issue. In contrast, we just employ the Busemann function but **without** the penalized term. In our experiments, we find that when using two considered complementary losses, penalizing the distance to the ideal prototype is unnecessary. Moreover, unlike Atigh et al. (2021) that **randomly** samples points from the unit sphere, we use **the predefined evenly distributed points** as ideal prototypes.

- We introduce a novel method to generate evenly distributed vectors (at the end of Sec 3.1) that can be used as both predefined prototypes in sphere space and as ideal prototypes in hyperbolic space. 

2. Second, we think that the representation learning aspect may have been overlooked. In Fig. 1, given an image I, we compute $z=f_e(I)$ while $f^S_p$ and $f^H_p$ are only two non-parameter projection operators: $z^S=f^S_p(z) = z/norm(z)$; $z^H=f^H_p(z)=exp_0(z)$ (defined in Sec 3.3 and 3.4). We only have a single vector $z$ which conveys the rich information extracted from image I, not two. Our approach involves pushing $z^S$ and $z^H$ towards the predefined/ideal prototypes as part of the learning objective for $z$.


- We further evaluate the performance of feature representations learned with different losses using CIFAR10/100 with ResNet-18 backbone in **Tab. 6 in the revised paper** (considering larger backbones/datasets for future work).  with our predefined prototypes outperforms Atigh et al. (2021).  surpasses , and ``Ours'' performs the best, proving the strength of our learned features in image classification tasks.

Dataset  | $L_{CE}$  |  Atigh et al. |  $L_{sCL}$  |  $L_{H}$ + our prototypes | $L_{S}$  | Ours 
---|:---:|:---:|---:|:---:|:---:|:---:
CIFAR10 | 94.9 | 92.3 | 95.0  | 94.1 | 95.2 | 95.4 
CIFAR100 | 76.1 | 65.8 | 76.3  | 68.0 | 76.7 | 77.8 
 
- It is worth noting that supervised contrastive learnings are two-stage approaches. During inference, they discard the projection layer obtained during training and require training additional linear layer(s) (using cross entropy in the original paper, cross-entropy). In contrast, our method is one-stage. Once trained either with ,  or both (using predefined ideal prototypes), we can use the network as such (without removing any layer or training additional layer). Classification is directly done by comparing the output of network with predefined prototypes.

Reviewer comment (Theory):
The authors only emphasize that the Euclidean-based distances appear sub-optimal for faces as the complexity and nature of human faces go beyond a basic Euclidean manifold. This explanation is vague and general, thus not convincing.

Author response:
Thank you for your suggestion. We developed the explanation in page 1. ``Unlike the assumptions of a Euclidean manifold, which presumes a flat and linear space, the intricate shapes and curvatures of facial features such as the nose, eyes, and mouth defy adequate description through Euclidean geometry alone. The human face is not a rigid structure; facial expressions are non-linear deformations of facial features (e.g., when a person smiles, the shape of the face undergoes complex transformations). In other words, human faces challenge Euclidean geometry with their dynamic and non-linear features.''

Reviewer comment (Theory):
Using hyperbolic space representations always works well for tasks with hierarchical relation nature. However, the authors fail to explain the inherent hierarchical relations in deepfake detection tasks.

Author response:
We have already conducted the experiments to demonstrate the hierarchical relations in deepfake detection datasets. **Originally located in the Appendix, we have now integrated them into the main paper**. Please refer to Figure 2. Left: Inherent hierarchical structure of different manipulations in FF++ represented as a tree. Right: t-SNE projection of embeddings for fake images from the FF++ in the Poincaré ball model. Dotted line circles indicate leaf nodes. CTru effectively captures coarse semantic differences closer to the origin in hyperbolic space (e.g., Reenactment vs. Replacement), with finer-grained distinctions positioned farther away (e.g., Face2Face vs. NeuralTextures)

Reviewer comment (Novelty):
Novelty: The paper integrates several existing techniques widely used in the computer vision community for the application of fake face detection, with no theoretical justification. Why does such an integration work? Why not other ways? This is one of my major concerns as a publication in ICLR, as to me I feel learning nothing from the paper.

Author response:
Dear reviewer,

Thank you for providing constructive reviews. We have addressed each of your questions individually in our response and hope these clarifications contribute to an improved rating. We have revised the paper according to your feedback, marking the changes in magenta. Additionally, we've highlighted in brown the text that was either part of the initial submission or present in the Appendix, which may have been overlooked.

**Question. Novelty: The paper integrates several existing techniques widely used in the computer vision community for the application of fake face detection, with no theoretical justification. Why does such an integration work? Why not other ways?**

**Reply:**

1. First, we believe that our approach goes beyond a simple combination of two known learning methods. In relation to existing works, different novelties (new loss terms, new ideal prototypes with improved properties) were considered in each space. Additionally, a new method for generating predefined/ideal prototypes was proposed. 

- In hypersphere space (Sec. 3.3), novel terms (the first terms in Eq.3) were incorporated instead of relying solely on the well-known supervised contrastive loss (sCL). This results in improved performance, as demonstrated in Tab.4.  In essence, $L_S$ differs from and outperforms $L_{sCL}$.

- In hyperbolic space (Sec. 3.4), we already clarified the distinctions between our method and that of Atigh et al. (2021) in Appendix (we moved it to the main paper). The main contribution of Atigh et al. (2021) is to integrate a penalized term into the Busemann function to avoid the overconfidence issue. 
In contrast, we just employ the Busemann function but **without** the penalized term. In our experiments, we find that when using two considered complementary losses, penalizing the distance to the ideal prototype is unnecessary. Moreover, unlike Atigh et al. (2021) that **randomly** samples points from the unit sphere, we use **predefined evenly distributed points** as ideal prototypes.

- We introduce a novel method to generate evenly distributed vectors (at the end of Sec 3.1) that can be used as both predefined prototypes in sphere space and as ideal prototypes in hyperbolic space. 

2. Second, we think that the representation learning aspect may have been overlooked. In Fig. 1, given an image I, we compute $z=f_e(I)$ while $f^S_p$ and $f^H_p$ are only two non-parameter projection operators: $z^S=f^S_p(z) = z/norm(z); z^H=f^H_p(z)=exp_0(z)$ (defined in Sec 3.3 and 3.4). We only have a single vector $z$ which conveys the rich information extracted from image I, not two. Our approach involves pushing $z^S$ and $z^H$ towards the predefined/ideal prototypes as part of the learning objective for $z$.

- We further evaluate the performance of feature representations learned with different losses using CIFAR10/100 with ResNet-18 backbone in **Tab. 6 in the revised paper** (considering larger backbones/datasets for future work). $L_H$ with our predefined prototypes outperforms Atigh et al. (2021). $L_S$ surpasses $L_{sCL}$, and ``Ours'' performs the best, proving the strength of our learned features in image classification tasks.

Dataset  | $L_{CE}$  |  Atigh et al. | $L_{sCL}$   | $L_{H}$ + our prototypes | $L_{S}$ | Ours 
---|:---:|---:|---:|---:|---:|---:
CIFAR10 | 94.9 | 92.3 | 95.0  | 94.1 | 95.2 | 95.4 
CIFAR100 | 76.1 | 65.8 | 76.3  | 68.0 | 76.7 | 77.8 
 
- It is worth noting that supervised contrastive learnings are two-stage approaches. During inference, they discard the projection layer obtained during training and require training additional linear layer(s) (using cross entropy in the original paper, cross-entropy). In contrast, our method is one-stage. Once trained either with $L_H$, $L_S$ or both (using predefined ideal prototypes), we can use the network as such (without removing any layer or training additional layer). Classification is directly done by comparing the output of network with predefined prototypes.

Reviewer comment (Writing):
Writing: I am not clear how Eqs. 2 and 7 are implemented. Eq. 2 is for generating “high” quality fake images, but why is “high” quality? Eq. 7 is for making decisions, but “how”?

Author response:
**Question. Writing: I am not clear how Eqs. 2 and 7 are implemented. Eq. 2 is for generating “high” quality fake images, but why is “high” quality? Eq. 7 is for making decisions, but “how”?**

**Reply:**

Thank you for your questions.
- Concerning Eq. 2, we generated our synthetic images using a variety of augmentation techniques, akin to those utilized in contemporary deepfake detectors like [SBI] but our approach introduces only localized artifacts to the images. Due to the space limit, the specific augmentations were detailed in the Appendix and cross-referenced in the main paper. By employing an array of sophisticated augmentation techniques—spanning spatial and frequency transformations, scaling, contrast adjustments, down-sampling, sharpening filters, blending, and more—we effectively simulate diverse fake artifacts.

**In Appendix**. “The following hyperparameter values were selected in a way that resulted in visually subtle artifacts, similarly to Shiohara & Yamasaki (2022). Specifically, we applied to “source”: (i) random scaling (followed by center cropping) by up to 5%, (ii) random shifting of HSV channel values by up to 0.1, and (iii) random translations of up to 3% and 1.5% of the image width and height, respectively. We also used more fine augmentations to “target”: (i) random shifting of HSV channel values by up to 0.3, (ii) shifting of RGB channel values by up to 20, (iii) random scaling of the brightness and contrast by a factor of up to 0.1, or (iv) down-sampling by a factor of 2 or 4, (v) a sharpening filter and blending with the original with an α value in the range [0.2, 0.5], (vi) JPEG compression with a quality factor between 30 and 70.”

- Concerning the use of fakeness score for detecting fake during inference, we follow the standard evaluation protocol for deepfake detection and report the Area Under the Curve (AUC) as the performance metric. We consider the Receiver Operating Characteristic (ROC) curve of the validation split of the FF++ dataset and select the threshold that yields the highest AUC. This threshold remains fixed when evaluating the testing dataset.

Reviewer comment (Evaluation):
Experimental results are slightly higher than the approaches that all are before 2023. Not sure if they are state-of-the-art.

Author response:
**Question. 3. Experimental results are slightly higher than the approaches that all are before 2023. Not sure if they are state-of-the-art.**

**Reply:**

Thank you for your questions.

- At the time of submitting the CTru paper, **we had already carefully verified** the SOTA results on the GitHub
https://github.com/Daisy-Zhang/Awesome-Deepfakes-Detection which gathers and updates almost all the deepfake papers from CVPR, ICCV, ICLR, NeuRIPS, and so on. In our initial submission, we already included the best known results so far coming from SeeABLE (at that time, the paper was just on arXiv and we now update its official publisher ICCV’23).

- For a clearer comparison, we have now  incorporated in the revised version the results of other recent methods from CVPR’23, ICCV’23 papers in Tab. 1 (please note that those ICCV’23 papers were published after the submission deadline of ICLR 2024).


Method     |       Celeb-DF-v2   |       DFDC	|      DFDC-p      |    Note       
---|:---:|:---:|:---:|:---: 
IIL - CVPR’23 [1]'s Tab.3            |      			     -*	         |           -	   |      73.8 | -
IIL + SBI (CVPR’23 + CVPR’22 Oral) [1]'s Tab.6       |    	     -*        |                      -	 |           79.6     |    Combine 2 methods
IID (CVPR’23) [2]'s Tab.2                      |              		    82.0      |                      -	 |           81.2       |  -
TALL + Swin (ICCV’23) [3]'s Tab.2       |                                  90.7      |                    76.8    |            -     |     x5 paras + 14M images
TALL + ViT-B (ICCV’23) [3]'s Tab.2  	 |  		    86.6	 |  	     74.1	    |          -	 |       x5 paras + 14M images
TALL + EffNet (ICCV’23) [3]'s Tab.2        |                              83.4 	 |  	     67.1	      |        -    |    + 1M images 
SeeABLE EffNet (ICCV’23) 		      |               87.3	 |  	     75.9  	  |         86.3   |  
CTru   (EffNet)    				       |              89.4	 |  	     77.0	   |        87.9  |  

*: IIL ([1]-Tab. 3) was evaluated on Celeb-DF-v1 which is older and easier than v2.
                                       
- Note that TALL [3] requires an additional large-scale dataset to pre-train its backbones (1M and 14M images when using CNN EffNet and Transformer Swin backbones, respectively), while CTru is trained from scratch with only 720 images. It is clear that CTru’s results are SOTA. We believe that our improvement is **not slight**. In comparison with SeeABLE, CTru consistently outperforms by a margin of nearly **2 points**, while maintaining a **faster and more generic** approach (without the requirement of a specific training scheduler and prior knowledge of the face, as mentioned in the paper).
- When comparing to TALL (ICCV’23), using the same EffNet backbone, CTru demonstrates significant superiority with improvements of **+6 and +10 points** on the CDF-v2 and DFDC datasets, respectively. Notably, CTru even surpasses TALL when *using Vit-B pretrained on Image-Net21k, comprising 14 million images and five times* as many parameters as CTru.

[1] Dong et al., “Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization, CVPR 2023.

[2] Huang et al., “Implicit Identity Driven Deepfake Face Swapping Detection”, CVPR 2023 

[3] Xu et al., “TALL: Thumbnail Layout for Deepfake Video Detection”, ICCV 2023