Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: In this paper, the authors conduct an early study of the detection problem of diffusion model deepfakes. They prove that existing of-the-shelf deepfake detectors trained on GAN-generated images generalize badly to DM-generated deepfakes, while they can be effectively adapted to DM domain through fine-tuning. The authors further analyze the differences between the fake images generated by GAN and DM in the frequency domain, showing that DM-generated images exhibit unique characteristics.
strength_and_weaknesses: Strengths:

1.  High-fidelity fake images generated by recent diffusion models have become a new threat in deepfake area. This paper presents a pioneer, systematic examination of this scenario with well-designed experiments.
2.  The authors provide several interesting observations from two different views: 1) the feasibility of detecting DM deepfakes using existing methods and 2) the characteristics of DM-generated images in frequency domain. These observations prove that DM deepfakes are more challenging to detect and have quite unique characteristics compared to GAN deepfakes, which brings useful insights to both deepfake detection and DM-based generation community.
3. The paper is overall well-written. All experiment details are presented clearly in the manuscript and supplementary materials with elaborate analysis.

Weaknesses:

Most of the main experiments are relatively simple and trivial. More in-depth analysis could be done to obtain richer and more complete conclusions. Specifically, I have the following suggestions:

1. In section 5.2, the authors make an interesting observation that detectors trained on DM-generated images can generalize better to GAN deepfakes than vice versa. They thus hypothesize that this is because detecting DM deepfakes is a more challenging task. Although the hypothesis seems to be supported by frequency domain analysis, a more intuitive interpretation might be achieved by further analysis on the feature space, e.g., a comparison between distributions of DM/GAN-generated features extracted by DM-trained detectors and GAN-trained detectors.

2. In section 6.2, the authors compare the effectiveness of detecting DM and GAN deepfakes in frequency domain using a simple logistic regression approach. However, some existing deepfake detection works absorb frequency info in a more 'soft' way, e.g. [1]. These frequency-aware deepfake methods' performance on DM deepfakes could also be examined. Will they perform better or worse on DM deepfakes than GAN deepfakes? Will the conclusion made in section 5.1 and 5.2 keep the same?

3. Most existing deepfake datasets (FF++, DFDC, DF1.0, etc.) mainly deal with forgery on human faces, which is an important and meaningful deepfake scenario. However in this paper, most conclusions are made on the LSUN bedroom dataset. The only face-relevant result (FFHQ) is presented in supplementary materials, but from Figure 18(e) we can observe a totally different pattern that contradicts the authors ' conclusion on high-frequency. Unique patterns of FFHQ can also be seen in Figure 16-17. I therefore suggest that the human face deepfake generated by DM should be discussed separately, and its uniqueness should be explained.

4. Since the training and inference of diffusion models are highly related to noise, the effect of perturbations widely used in deepfake datasets (Gaussian noise, blurring, compression, etc.) on DM deepfake detection might also be interesting to look at.


Reference

[1] Luo, Yuchen, et al. "Generalizing face forgery detection with high-frequency features." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.
clarity,_quality,_novelty_and_reproducibility: - Clarity: Excellent. The paper is clearly presented.
- Quality: Average. The experiments and analysis are detailed and reasonable, but some further in-depth evaluations could be done.
- Novelty: Fair. The topic is new, but no novel methods are presented.
- Reproducibility: Excellent. Implementation details are described clearly, together with abundant supplementary materials.
summary_of_the_review: This paper gives the first investigation into the deepfake detection problem on diffusion model-generated images. The authors present several meaningful insights into the unique properties of DM-generated deepfakes through well-designed experiments and detailed analysis. However, more in-depth discussions could be done to make the conclusions richer and more complete.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper evaluates the performance of deepfake detectors on images generated by five diffusion models (DM) and five GANs. It also analyzes the spectral representation of DM-generated images.
strength_and_weaknesses: (+) The paper is an easy-to-read paper evaluating how well images generated by DM can be detected The paper evaluates the performance of existing deepfake detectors on images generated by DM. It reports performance results of finetuned existing deepfake detectors to DMs. 
(-) The paper does not provide any novelty but instead provides more information regarding DM and compares detection results with GAN-generated images. The paper analyzes the spectral content of DM-generated images comparing GAN-generated images.


clarity,_quality,_novelty_and_reproducibility: The paper is easy to read and provides important information for a better understanding DM, and although the information is important, the paper does not provide any novelty.
summary_of_the_review: The paper is easy to read and provides important information for a better understanding DM, and although the information is important, the paper does not provide any novelty.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper presents an in-depth review of detection of diffusion-model generated images. This paper evaluates SOTA detectors on a range of DMs and then analyzes the differences of many aspects between DM-generated images and GAN generated images in addition to image fidelity. 
strength_and_weaknesses: The experimental setup of this paper is reasonable and adequate. The writing is clear and smooth. The results and conclusions are intuitive. I like how the authors present their results by asking questions and leading readers to answers.

I do not observe obvious drawbacks of this paper. It might make sense to include some results that can be easily classified as fake or misclassified from different approaches. This can intuitively show the features of those generated images that are confidently classified as fake.
clarity,_quality,_novelty_and_reproducibility: This paper is written well and experiments are sufficient. It investigates an area that has yet been explored by previous approaches, thus I think it is novel. Most models used in this approach are from official public resources, so reproducibility seems feasible.
summary_of_the_review: I think the topic of this paper is meaningful and useful to a decent group of people. Conclusions are clear. And the experimental setup is well-rounded. In general, it is a good paper and I vote for acceptance.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: In this paper, the authors focus on studying the detection of images generated by diffusion models. Specifically, this paper evaluates the performance of current methods and then analyzes the difference between GAN-generated and DM-generated images.
strength_and_weaknesses: Strengths:

1.	This paper focus on an ignored problem, the detection of DM-generated images.

2.	A benchmarking of the DM deepfake is conducted.

3.	This manuscript finds that the DM-generated images are different from GAN-generated images.


Weaknesses:

1.	There exist various generated models. However, this paper just analyzes the DM-generated and GAN-based models.

2.	This paper finds that existing detectors fail to effectively recognize DM-generated images and gives some suggestions. Unfortunately, they do not propose an effective method for deepfake detection.

3.	Only three detection methods are benchmarked in this paper. It is recommended to study more methods.

clarity,_quality,_novelty_and_reproducibility: The Clarity and Reproducibility of this paper are good. However, the Quality and Novelty are limited (see the above section).
summary_of_the_review: This paper focuses on an ignored problem. However, the novelty is limited and the experiments are inadequate.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper provides extensive experimentation on how state of the art deepfake detectors behave on diffusion model generated images.
They also propose an analysis in the frequency domain of diffusion model generated images compared to GAN and real images.

strength_and_weaknesses: Strength:
- extensive experimentation on said state of the art deepfake detectors
- extensive experimentation on the influence of DM hyper parameters (sampling steps)
- discussion on the optimization target of these deepfake detectors

Weaknesses:
- need more maturation on a innovative methods to detect DM deepfakes
clarity,_quality,_novelty_and_reproducibility: This work is very original in the experimentation part.
It has not the standard ICLR structure as it is more a refutation paper but it is a worthy work.
summary_of_the_review: While the experiments and their interpretation are very interesting in trying state of the art detectors on DM fakes, this works lacks maturity in providing a novel approach to detect DM fakes.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper was reviewed by three experts in the field. The AC and reviewers had a thorough discussion on this paper, where the reviewers raised many concerns regarding the paper: 1) The observations and assumptions in this paper might not be generalizable across different domains (e.g., faces, indoor, and outdoor). 2) The experiments are not comprehensive enough regarding the various types of diffusion models. Considering the reviewers' concerns, we regret that the paper cannot be recommended for acceptance at this time. The authors are encouraged to consider the reviewers' comments when revising the paper for submission elsewhere.

justification_for_why_not_higher_score: The observations and assumptions in this paper might not be generalizable across different visual domains. Therefore, comprehensive investigations are needed to make the findings more convincing.
Decision: Reject