Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The authors propose a framework to improve the generalization and robustness of deepfake detection, which relies on local low-level and temporal information, and transformer-based model. In particular, the Local Sequence Transformer (LST) is used to identify low-level temporal inconsistency and the Cross-Patch Inconsistency loss (CPI) is used to model spatial inconsistency. The Global Contrastive Classification (GCC) is used for final classification, which inserts temporal tokens and adopts additional three Transformer blocks.
Quantitative experiments show better results on four datasets in generalization evaluation benchmark and on seven perturbations in robustness evaluation benchmark, compared with some recent works. 

strengths_and_weaknesses: Strengths:
1. The paper is well written, easy to follow and provides adequate experimental results.
2. The proposed framework successfully integrates temporal information and low-level features, which are separate from improved vision transformer and shallow CNN, and could be of some contributions to the community.
3. There are some novel ideas in the LST module: the first is the input type: ‘local patches with same spatial position’, and the second is the low-level temporal enhancement module, corresponding with the research that the low-level artifact is more suitable than the semantic artifact for deepfake detection. What’s more, the CPI module is well designed, and the motivation of the GCC module shows good understanding of deepfake datasets.
4. The evaluation experiments of generalization and robustness are well done.

Weakness：
1. In Sec 3.2, the authors use ‘shallow 3D convolution’ in the LST module for ‘align’ as the first reason (Ignoring the second reason temporally). In my opinion, it is similar to face alignment, so if face-alignment operation plays the same role，why not just do it in the pre-processing stage.
2. In Sec 3.3, sim_gt is calculated by interpolation operation, how about max or average pooling operation? The authors may provide ablation study about how to calculate it.
3. In Sec 4, the experiment part is lacking intra-evaluation, for example, both training and testing on FF++.
4. In Sec 4.1, how many frames or clips are sampled from videos in each deepfake dataset. 
5. In Sec 4.3, the result analysis about robustness evaluation seems not enough.
6. In Sec 4.4, the ablation study is only done for generalization evaluation, that for robustness evaluation is also required.

questions: Confusion:
1. In Sec 3.3, it seems that the supervision information has not only binary labels but also masks, so the prediction may be determined by other simpler structures like fine-grained classification layers, instead of the proposed complex “temporal tokens + three Transformer blocks” structure.
2. Based on Table 3, the result line from the ‘LTTD w/o CPI’ shows that CPI contributes far less than the LST or GCC module in DFDC and DeepFo. So is the CPI module significance?

Limitation:
1. For the authors’ motivation, using low-level features instead of semantic features benefits a lot in Deepfake Detection. It seems that the middle-level features, and the suitable mixture of low-& middle-level features may also contribute to Deepfake Detection. It is a pity that the paper does not include this exploration.
2. For Sec 4.4, the robustness evaluation seems not strong enough, the authors could refer to the benchmarks in Improving [45] and ForgeryNet [CVPR 2021, Forgerynet: A versatile benchmark for comprehensive forgery analysis]，whose robustness evaluation contains more types of perturbations and mix perturbation test sets.

limitations: The authors have done it properly.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper proposes a Local- and Temporal-aware Transformer-based Deepfake Detection (LTTD) framework to capture temporal cues from local sequences. The authors design a Local Sequence Transformer (LST) models the temporal consistency of restricted spatial regions to learn local-to-global features. The proposed LTTD framework achieves outstanding generalizability and robustness.
strengths_and_weaknesses: Strengths:
1. Clear description of method.
2. Remarkable generalization towards unseen deepfakes and strong robustness against post-processing operations. 

Weakness:
1. Novelty.
2. Analyses.

questions: 1. Novelty. 
Many studies prove that the low-level patterns and temporal inconsistency are effective clues for Deepfake detection. What is the motivation of this search? Does the LTTD framework address specific problems that previous methods have neglected? The authors are supposed to underline the motivation and advantage of the proposed method.

2. Analyses. 
1) The novel Low-level Enhancement is designed by shallow 3D convolutions. It can be regarded as an 3D extension of textural enhancement [60]. The authors should validate whether the 3D convolutions have the major contribution of improving feature learning. Can it be implemented with 2D convolutions to have comparable performance gain and less computational overhead?
2) The LTTD focuses on the low-level temporal patterns of restricted spatial region. What is the restricted spatial region? Please introduce the definition of restricted region. Furthermore, is it necessary to restrict specific regions? The authors should analyze the difference between restricted region and unrestricted region.
3) As shown in Table 3, the ViT [18] has inferior performance to the CNN baseline Xception. What is the reason for this phenomenon? How do the ViT-based methods FTCN [62] and LTTD improve the performance of ViTs on Deepfake detection? What is the key to this significant improvement?
4) Complexity. The comparisons on model complexity and GFLOPs between LipForensics, FTCN, and LTTD are desirable.
5) Localization. Can the LTTD localize the forged regions? It will be explicit to visualize the localization of face forgery.

limitations: Adequately claimed.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: Briefly summarize the paper and its contributions. This is not the place to critique the paper; the authors should generally agree with a well-written summary.

To achieve generalizability across deepfake methods and robustness towards image post-processings, this paper proposes the Local- & Temporal-aware Transformer-based Deepfake Detection (LTTD) framework. The experiments show that the proposed approach achieves the state-of-the-art generalizability and robustness.
strengths_and_weaknesses: Strengths:
+ The paper attempts to ensure both generalizability and robustness in deepfake detection, which are two important problems in deepfake detection. The proposed method focuses on low-level temporal learning and prevents overfitting to global semantic cues.

+ The experiments are comprehensive.

+ The paper is clear to understand and the writing quality is relatively good.


Weaknesses:
- The paper claims that the method crops the face regions using the same bounding box randomly determine the clip range in-the-fly (L246), which may affect the practicality. It may be suitable for the videos in datasets because many of these videos often have a single scene. However, some videos in datasets and most videos in wild often have many scenes. If the clip contains scene switching, it may cause face misalignment and affect the detection results.

- Some details of selecting video clips are missing. How many clips extracted from one video? In FaceForensics++, one video may have less than 300 frames or more than 1000 frames. For short videos and long videos, if the number of clips different? The paper report video-level AUC Why the temporal dimension is 16? It is necessary to have an ablation study to see the effect of different choices of this parameter.

- The method splits the input image into local patches and use 3D convolutions. It seems that the framework needs complex computing. What about the efficiency of inference?

- It is not clear that how to compute the ground truth similarity matrix and how to generate the mask sequence (L201-205), especially the fake clip.
questions: See above.
limitations: This work aims to detect deepfake videos, which is important to prevent the spread of fake information.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The proposed method combines 3D conv and vision transformer to extract forgery artifacts in Deepfakes videos, the experiments prove that the proposed method achieves outstanding generalization performance on multiple Deepfakes datasets. 


strengths_and_weaknesses: Strengths:
The overall performance of the proposed method is outstanding.
Weaknesses:
The writing of the manuscript should be improved.
questions: 1. In the discussion part for Section 3.2, the authors claim that the proposed structure which combines 3D conv feature and self-attention feature can explicitly avoid semantic modeling of features like facial structure and always focus on low-level temporal learning. But no explanations are given to prove this statement.
2. Comparing Eq.4 and Eq.8, the formats of these two equations are not unified. In addition, the meaning of the symbols used in the equations is not clearly given, making the equations hard to understand. Also, it is strange to use t (lower letter) to represent the features after the pooling operation while the upper letter T represents the time sequence. Similar problems occur in all the equation parts.
3. In equation 12, in my understanding, the m should equal Flatten(mβ)
4. The authors mention that the temporal dimension T is set to 16. However, no selection details are given. Suppose we have a 300 frames Deepfakes video with the last 150 frames tampered. The frames selection strategy would be crucial in detecting this Deepfakes video. In addition, no discussions for frame number selection are given. The authors should present more analysis on how frame number influences the proposed structure.
5. The following method is not compared in generalizability evaluation:
Kaede Shiohara, Toshihiko Yamasaki: Detecting Deepfakes with Self-Blended Images. CVPR 2022. 
limitations: The authors have adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The paper proposes a method for DeepFake Detection. The method focuses on a particular way of modeling the temporal aspect of the detection: instead of learning first spatio-temporal downsampled embedding with 3D convolution and then aggregating the embedding with transformers [62], the proposed method immediately breaks the image into patches and adds the temporal dimension. Unlike prior work, temporal modeling is done directly over patches with the motivation of identifying low-level temporal inconsistencies and standing on the shoulders of insights from [35]. This is achieved by moving the transformer architecture immediately upstream in the pipeline contrary to previous methods in which usually temporal aggregation is performed downstream. Though the method models patches in this way still use part of 3D convolutions applied to patches. The method is supervised with standard cross-entropy loss and another loss that enforces that real patches should be similar to each other in the learned embedding space.
Experimental results are provided on recent benchmarks for Deepfake detection along with ablation studies. The robustness of the method to common perturbation is also tested.
strengths_and_weaknesses: ### Strenghts
- The method is overall motivated when proposing something and takes a direction that is different than the state-of-the-art [62] of directly modeling patch sequences bringing transformers upstream in the pipeline.
- Overall the innovative claims in the intro are well balanced with the paper's experimental evidence, especially in the robustness part of perturbations.
- I was able to grasp the overall idea of the method quickly though often parts feel overcomplicated in the way they are written.
- The experimental validation is promising and uses recent benchmarks for deepfake detection. Ablation studies are provided to support design choices. In some cases, the ablation improvement is clear in other cases less clear. In Tab. 3 an overall summary using average with standard deviation across unseen manipulation is needed.


### Weaknesses

- The average improvement over the competing method [62] `(89.6% vs 91.8%) `is appreciated but looks a bit marginal without comparing the timings of the approach. Also, it is unclear if the improvement `+2.2` video-level AUC is statistically significant. What happens if you train multiple **LTTD** for different initialization and multiple **FTCN-TT** and report the average and standard deviation? Is the improvement statistically significant, given the standard deviation of the two approaches? (Assuming authors have code to run FTCN-TT). I ask this because, it is clear that from the distribution of performance the gap is obtained thanks to the DFDC dataset, all the others are basically the same. What makes LTTD works well on DFDC? This small improvement questions the overcomplexity of the method and I have the idea that the paper will be hard to reproduce and re-implement.
-  Though the method is well-motivated, it feels a bit the method is riding the success of transformers and applied to the idea of modeling temporal discrepancy with patches for DeepFake Detection, changing the idea of [62]. Also, though the method seeks a changing paradigm does still apply 3D convolution as in [62]. This part where the method applies 3D convolution on patches is less clear. Convolution works on images because of the assumption that patches can be highly correlated but here the method breaks the image by splitting into patches, so more explanation on this 3D convolution is needed for the way it handles the spatial component over patches.
- The idea to break the image in patches does not account for rigid and non-rigid deformation that the face may even in 3D, convolution suffers from this too but is more resistant to jittering of translation by definition. The paper says something on this matter at `L149` but is not clear.
- `L242` gives details about the face cropping which is related to alignment, motion compensation, and breaking the image into patches. It is not clear overall how the face cropping of the system works. The method does not do frame-by-frame face detection and cropping but uses a single crop. The text says using `the same bounding box` but same respect to which frame? It is not clear also what is randomly determined. If a single crop is used and the face move slightly this is even worse than the random jittering that you get by frame-by-frame processing.
- More on this: In section 3.2 it is not clear if the Conv3D still slides over the patches or if the spatial kernel size of the convolution is big as the patch itself. This part spends a lot of space with equations (1-4) with tons of super and subscripts but it does not make the reading easier.
- It is ok to be formal with the notation yet very often the article abuses notation and this makes the article not easily readable.
There are multiple of these remarks e.g. usage of subscript that is not necessary $ \mathbf{x}_{p}^{i}$. Why $p$ is used in L.131, L144 I believe the patch is indexed by $i$. Same remarks for Eq. (5-8).
- Given that the title includes _Delving into Sequential Patches_, the method misses an instructive study of what happens when the temporal dimension $T$ varies from different values than 16; also, given a fixed $T$, the paper misses a study to understand whatever it is better to have a dense sampling of the frames (e.g. 1:1 wrt to the original video) and a small window or maybe more sparse sampling of the frames (1:20) but to aggregate more content of the video.
- Fig. 1 is overall nice and I appreciated but it is pretty dense and not easy to digest immediately.
- I found some typos and sometimes the text use adjective a bit off: e.g. `L17 ` `enormous fake videos`? This means that a video is huge, big in size. `L81` data aRgumentation. `L199` vise versa (vice versa). `L308` a unified manifolds. 

### Justification of the rating
My opinionf the paper is that it is a good system paper for deepfake detection with marginal improvement over the state-of-the-art, assuming these are statistically significant. The paper has some strength in showing more robustness than other methods. The overall claims are decently supported. Besides that, the paper needs to be improved by simplifying the notation by making it more clear for the community since as of now it will be hard to reproduce it. Overall it is a good paper but I believe that if the paper is not accepted, the deepfake community is not going to lose innovative ideas. According to this, I sugged a borderline reject score before rebuttal.
questions: - Tab. 3 maybe improve if an average is presented.
- No detail of training and testing time.
- What are the parameters of the 3D Convolution used? Kernel size over spatial dimension and temporal.

- There are several parts that are not clear:
   > L198: the simplest thought is that the real region should be similar to the real one to some extent

For me, this is not enough to justify the loss and is not a very clear sentence. The sentence says that real regions should be similar to real ones but this is not true in the image space; maybe this is the requirement that the loss is imposing in the learned feature space though is not clear. Furthermore, it is not clear what the loss why the loss matches the cosine similarity between all patches with the ground-truth similarity matrix. Which min and max values take the ground-truth similarity matrix?
The process of generating the ground-truth similarly matrix is displayed in Eq. (12,13) but seems over complicated with the notation ($m_\alpha,m_o,m_\beta,$). Why does interpolation have to be $\pi$? Per my understanding, the min and max values for sim_gt are $\{-1, 1\}$. Is this correct? Anyway, the motivation for the loss is that real features should be basically similar to each other but it is not clear how this is attained given the current loss formulation since there is no selection of the real embedding in the loss unless this is achieved with the $sim_git$ though how? 

 L:208 $\mathbf{x}_{class}$ is mentioned without explaining what is it.

L:242, In the same way, I mentioned in the weakness the part on data processing is hard to understand. Especially this sentence:
>  Therefore, in our method, we crop the face regions using the same bounding box after randomly determining the clip range in-the-fly,




limitations: The limitations presented are somewhat related to the generalization of the model. It says _now it is good (at least a bit superior to the state-of-the-art) but we will be unsure in the future._ 

I think an overall direction for the future that would be nice for Deepfake Detection is working toward ensuring that the prediction of the Detection is really calibrated well. To the best of my knowledge, there is no detection system yet deployed that can be left free of running on video on social networks and flagging videos. I am not sure if there are implementations with humans in the loop that check highly confident video marked by the detector. Anyway, the point is that, given that an erroneous highly confident prediction as fake for an authentic video could be even more problematic of a deepfake itself, all these deepfake detectors have the limitation that we do not know if they are _calibrated well_.  Perhaps adding a general discussion on this may improve the paper: i.e. as of now, the method has good generalization accuracy, but if we run it in the wild on videos for which we do not have labels, how much can we trust the prediction?
review_title: null

Meta-review: metareview: All reviewers are positive about this paper. Generally speaking, the proposed method is novel and is also easy to follow due to well writing. Also, the experiments are comprehensive. In the rebuttal, the authors also provide some qualitative results to clearly respond to the concerns of reviewers. So, I suggest accepting this paper. 
confidence: Certain
Decision: Accept