Aligned Datasets Improve Detection of Latent Diffusion-Generated Images

Authors: Anirudh Sundara Rajan, Utkarsh Ojha, Jedidiah Schloesser, Yong Jae Lee
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Image Forensics, Latent Diffusion

Abstract:
As latent diffusion models (LDMs) democratize image generation capabilities, there is a growing need to detect fake images. A good detector should focus on the generative model’s fingerprints while ignoring image properties such as semantic content, resolution, file format, etc. Fake image detectors are usually built in a data-driven way, where a model is trained to separate real from fake images. Existing works primarily investigate network architecture choices and training recipes. In this work, we argue that in addition to these algorithmic choices, we also require a well-aligned dataset of real/fake images to train a robust detector. For the family of LDMs, we propose a very simple way to achieve this: we reconstruct all the real images using the LDM's autoencoder, without any denoising operation. We then train a model to separate these real images from their reconstructions. The fakes created this way are extremely similar to the real ones in almost every aspect (e.g., size, aspect ratio, semantic content), which forces the model to look for the LDM decoder's artifacts. We empirically show that this way of creating aligned real/fake datasets, which also sidesteps the computationally expensive denoising process, helps in building a detector that focuses less on spurious correlations, something that a very popular existing method is susceptible to. Finally, to demonstrate the effectivenss of dataset alignment, we build a detector using images that are not natural objects, and present promising results. Overall, our work identifies the subtle but significant issues that arise when training a fake image detector and proposes a simple and inexpensive solution to address these problems.

Decision: accept (poster)
Reviewer scores: 6, 6, 6, 6

Reviews and discussion:
<thread>
creation date:
1729440425548

thread_type:
official_review

<thread_object>
creator: reviewer_ecmv

<content>
summary:
The paper mainly focuses on detecting the fake images generated by latent diffusion models (LDMs). It states that dataset alignment plays an important part in detecting LDM-based fake images and proposes a simple method to detect LDM-based fake images by training detectors with the aligned dataset (real images and fake images created by only LDM's autoencoder). Experiments show that the proposed method is inexpensive and effective in detecting LDM-based fake images.

strengths:
1. The proposed method is simple and can potentially be a useful baseline for LDM-based fake image detection.
2. The proposed method is more effective and efficient than the paper's major baseline (Corvi).
3. Extensive experiments provide sufficient support for the superior performance of the proposed method. 4. The paper is somewhat clear and well-written to me.

weaknesses:
1. The title slightly disqualifies the paper's main topic since the authors pay attention to LDM-based fake images. However, many generative models can create general fake images, including AutoEncoder, GAN, VAE, and LDM. It seems more appropriate to add qualifiers in the title. 
2. Both Corvi and the proposed method remain vulnerable to minor changes. Although the proposed method shows wonderful performance against some LDM-based images, the comparison between results from Table 1 and Table 2 reveals both Corvi and the proposed method can be easily disturbed by post-processing like lossy compression.
3. Lack of further insights between "Ours" and "Ours-Sync". In section 4 (Our Approach), two variants "Our" and "Our-sync" are introduced but with no extra explanations. In experiments, "Ours-Sync" shows superior performance in most cases than "Ours" with almost no analysis. 
4. The figure in the conclusion loses its caption. Help it.

questions:
1. Why does the performance of detectors drop a lot facing fake images with small scaling factors or real images with large factors? Are there any reasonable explanations?
2. Why does "Ours-Sync" outperform "Ours" in most cases, and why does "Ours" kick back in some special cases?
3. The combination of the proposed method and Corvi should be reasonable and available. Does it perform remarkablely in detecting LDM-based fake image detection?
4. It seems that the proposed method can enjoy a better performance with a large batch size. Would you mind a try?
5. I understand that using ResNet-50 is for alignment in the network architecture to some baselines, but more experiments on different network architectures are suggested.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730291374703

thread_type:
official_review

<thread_object>
creator: reviewer_caut

<content>
summary:
This paper investigates the effectiveness of dataset alignment in fake image detection, specifically for images generated by latent diffusion models (LDMs). The authors propose a simple approach: reconstructing real images using the LDM's autoencoder and treating these reconstructions as fake images. This aligned dataset allows for training a classifier to distinguish real from fake images, focusing on the generative model's artifacts rather than spurious correlations such as resolution or content differences. Experimental results show that this alignment method improves the robustness and generalization of the detector across different generative models, while also being more computationally efficient.

strengths:
1. The paper proposes an interesting framework for generating fake images by using only the LDM's autoencoder, rather than the full generative process, to train the detector. Experiments show this approach achieves better performance compared to using complete generated images, which is a noteworthy finding.

2. The paper reveals the impact of scaling factors on detection methods, contributing valuable insights to the field.

weaknesses:
1. Lack of technical contribution: Although the findings are interesting, the authors did not deeply explore the reasons behind the observed phenomena or how they could further benefit existing detection methods. They only conducted shallow comparative experiments using their generated data. Overall, the contribution feels insufficient.

2. Limited comparison methods: The authors aim to prove their dataset has better generalization and performance than others, but they only compared it with two methods. These methods are not state-of-the-art (SOTA) on complex public datasets like GenImage. The lack of sufficient comparisons weakens the paper's persuasiveness. Additionally, the comparison datasets are not from AIGC detection benchmarks, making it difficult to determine a fair performance improvement.

3. The proposed method of classifying only LDM-generated data can avoid some spurious features but also ignores certain important features, such as artifacts introduced during the diffusion process. The method is mainly effective for LDM-based generation techniques and less so for traditional GAN or diffusion models. It may be that the LDM itself removes diffusion artifacts, making it easier to detect LDM-specific artifacts. Moreover, the method cannot handle semantic inconsistencies left by some generative methods, such as incorrectly generated hands.

4. The authors should conduct robustness tests, such as robustness against noise or JPEG compression, to further validate the method.

5. The paper structure could be improved, with clearer diagrams and better formatting. The dataset descriptions are somewhat disorganized, and a more structured introduction is recommended.

Despite these shortcomings, I still believe this discovery is valuable, and the relative improvements are significant. I encourage the authors to further explore this phenomenon in depth.

questions:
See the weakness section.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730646915709

thread_type:
official_review

<thread_object>
creator: reviewer_hbmp

<content>
summary:
This paper is rooted in a key observation that training a naive binary classifier to detect real/fake might learn the spurious correlations, and thus cannot achieve a more robust detection result. To achieve the alignment of the training dataset, they propose a new dataset by using LDM’s autoencoder for reconstruction purposes. The authors also provide clear evidence and experiments to verify their observation.

strengths:
1. The paper is well-written and has a very clear research motivation.

2. The dataset alignment is a critical problem that might cause the model to learn spurious correlation in the fake image detection field.

3. The authors provide reasonable evidence to validate their findings and observations. Especially the effectiveness of downsampled before and after resizing (Table 5), which is insightful to me.

weaknesses:
- A major limitation of this work is the generality of images generated by Latent Diffusion Models (LDM). Does the detector primarily learn LDM-specific fake patterns, rather than a broader range of patterns such as those generated by GANs? Or does it learn only method-specific fake patterns unique to a particular LDM instance, failing to generalize across the LDM family? A more robust evaluation of this generality would be beneficial.

- This paper defines "fake image detection" as "entire image synthesis." However, this scope does not cover other types of fake images, such as deepfakes (e.g., face-swapping) or talking-head videos. A further discussion of the results of these types of fake images would enhance the paper.

- There is limited analysis of pre-trained models in this work. For instance, CLIP and ResNet-50 (trained on ImageNet) may detect fake images differently. Given CLIP’s rich knowledge, it may be less likely to focus on irrelevant patterns. A deeper analysis and discussion of these differences would strengthen the paper.

- What are the advantages of conducting online reconstruction using LDM (as in previous works, such as ref[1]) compared to offline reconstruction (as used in this work)?

- The alignment issue highlighted here is not entirely new; several prior studies, especially in deepfake detection, have addressed this challenge. For example, refs [2] and [3] discuss the importance of alignment for robust detector performance. Ref [2] shows that a resolution gap between real and fake images can bias models toward interpreting higher resolution as real and lower as fake, while ref [3] demonstrates that aligning real and corresponding fake samples in training improves detector robustness. Further discussion of these relevant works would help contextualize this study’s findings on alignment.

ref[1]: DIRE for Diffusion-Generated Image Detection, ICCV 2023.

ref[2]: DF40: Toward Next-Generation Deepfake Detection, NeurIPS 2024.

ref[3]: Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection, CVPR 2024.

questions:
1. Does the detector primarily learn LDM-specific fake patterns, rather than a broader range of patterns such as those generated by GANs? Or does it learn only method-specific fake patterns unique to a particular LDM instance, failing to generalize across the LDM family?

2. What is the clear and accurate definition of "fake image" in this work?

3. What are the differences between CLIP and Res50 in the shortcut manner?

4. Offline reconstruction and online reconstruction.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730689575669

thread_type:
official_review

<thread_object>
creator: reviewer_uw3n

<content>
summary:
This paper aims to detect fake images by improving the aspect of data alignment. The paper reconstructs all real images using the autoencoder of a latent diffusion model and then trains fake image detectors to distinguish between real and reconstructed images. The paper claims that this approach prevents the fake image detector from focusing on unwanted artifacts such as semantic content, resolution, or file format. Additionally, they argue that this method of data collection is more efficient than prior works which pair real images with generated images, as it avoids the expensive denoising process associated with generating fake images. The paper shows stronger results than prior works in detecting latent diffusion models and provides an analysis of potential pitfalls in prior works, such as detection bias stemming from resolution differences or compression artifacts between real and fake data.

strengths:
1. The paper proposes a simple and efficient way to collect data for fake image detection.
2. The paper provides an analysis of common pitfalls of prior works, such as image resolution or compression artifacts.

weaknesses:
1. It is unclear whether or not the proposed method would generalize well to architectures other than latent diffusion models. For example, would this generalize well to models such as VQ-VAE[1], VQ-GAN[2], or more recent models with very different autoencoder architectures? The paper mentions that it does not work well on models with vastly different architectures (e.g., FLUX.1-dev), but it is not clear to what extent of architectural changes the proposed method is robust to.
2. The proposed method, by design, disregards semantic content. While the paper claims that this is a desired property, in some contexts, the semantic content may matter. With proper techniques, the autoencoder artifacts may be decimated [3]. In such cases, the proposed method may fail to properly distinguish fake and real. However, prior data-driven approaches may be able to pick up on semantic cues such as over-saturation or contrast, malformed objects, or unrealistic sceneries to detect generated images. 
The performance differences after post-processing shown in Table 3, between detectors trained on natural images and shader images, may hint at the importance of semantics.
3. The paper lacks a detailed analysis of sensitivity to various transformations. The paper claims that their method of training detectors is more robust as it forces the detectors to focus more on autoencoder artifacts. However, the experiments do not adequately demonstrate this property, as the paper only compares the performance of a single randomized post-processing technique in Tables 2 and 3. Including a detailed, separate analysis of the robustness to various transformations, similar to those shown in Figure 2 and the figure on page 10 would better illustrate this characteristic and provide better insights for future research.
   - The figure on page 10 should be properly numbered and referenced in the main text.
4. Accuracy is not the best metric for showing the performance of the detectors, as it is sensitive to thresholds. I recommend the authors to consider threshold-less metrics such as average precision (AP). For example, in Tables 1 and 2, applying post-processing actually improves the accuracy of certain detectors (e.g., Cozzolino-LDM). Using threshold-less metrics such as AP may provide a more accurate picture of performance degradation caused by post-processing.

I will give borderline/weak accept for this round and update the rating after the discussion.

&nbsp;

***Minor comments:***
- The figure on page 10 lacks proper numbering.
- While the writing is clear, some of the writing seems less professional. Some examples:
    - Lines 026-027: "Just how effective ... can be"
    - Lines 121-122: "report limitations that even our method cannot circumvent"
    - Lines 274-275: "See how much more efficient our method ... is"
    - Lines 337-338: "Synthesizing a lot of images ..."
    - Lines 517-518: "This is where the promise of ... come"
    - I believe the writing quality could be improved if some of these writings were delivered in a more professional tone (e.g., "a large number of images" instead of "a lot of images").

&nbsp;

[1] Van Den Oord, Aaron, and Oriol Vinyals. "Neural discrete representation learning." Advances in neural information processing systems 30 (2017).

[2] Esser, Patrick, Robin Rombach, and Bjorn Ommer. "Taming transformers for high-resolution image synthesis." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12873-12883. 2021.

[3] Davide Cozzolino, Giovanni Poggi, Riccardo Corvi, Matthias Nießner, Luisa Verdoliva. "Raising the Bar of AI-generated Image Detection with CLIP". Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4356-4366

questions:
1. What is the rationale behind calibrating the threshold in Table 3? Why is this different than prior experiments (Tables 1 and 2), and what happens if you use the default threshold (0.5) similar to Tables 1 and 2?
2. I'm curious what the authors' intuition behind why the models trained on shaders dataset show more sensitivity to post-processing than the ones trained on natural images.
3. The Appendix A.2.1. shows that the proposed method can train the detector in a more data-efficient manner. I'm curious why this is the case. Is it because it only has to focus on single artifacts?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732002960521

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Authors' Response

comment:
We thank the reviewers for their time in giving us constructive feedback. It is encouraging that the reviewers found our analysis of the effects of scaling/compression operations in the context of fake image detection insightful (hbmp, caUt) and believe it will be valuable (uw3N) to the community. The reviewers also found the proposed solution to be interesting (caUt), simple (uw3N, ecmv) and efficient (uw3N, ecmv). Overall, the reviewers found the work to be well-written (hbmp, ecmv) with sufficient experiments to support the claims (hbmp, ecmv). 

Many reviewers raised questions about the effectiveness of our approach in detecting images from other kinds of generative models (like GANs). We want to provide a common clarification to that question: our goal is not to build a universal fake image detector that can detect a fake image from all possible breeds of generative models. Rather, our goal is to first highlight that even the more simple task - robustly detecting fake images within the same breed - is more difficult than previously thought, due to the presence of spurious features in the dataset, such as resizing artifacts (lines 290–302), which can be used to separate real and fake images and we propose a method to address this issue. Within that breed (LDM), there are many variants of generative models out there (e.g., changes in the UNet, VAE architecture). And we show that our method is indeed effective in detecting images from all of them; see Tables 1, 2. In particular, we show improvements in accuracy on detecting fake images over the state-of-the-art method of Corvi on recent latent diffusion models such as Playground, PixelArt, and Midjourney by +52.09%, +22.59%, and +21.91%, respectively. We will further clarify this distinctive goal in the final version of our paper.

Furthermore, we have addressed several important concerns raised by the reviewers in the Appendix of the revision,
1. **Threshold-agnostic metrics**: We report the Average Precision (AP) and True Positive Rate at 5% False Positive Rate (tpr@5fpr) in Appendix A.2.3. We achieve a very high AP and TPR@5FPR. For instance, when detecting Playground-generated images, we achieve an AP of 98.54% and a tpr@5fpr of 92.88%, compared to the next best method (Corvi), which achieves an AP of 90.93% and a tpr@5fpr of 65.75% on the same task.
2. **Impact of Architecture/pretraining**: We show that our approach has benefits in a manner which is agnostic to the network architecture/initialization. The results are present in Appendix A.2.4. The experiments demonstrate that the advantages of our approach persist across various architectures and initializations. For instance, when training a CLIP-initialized Modified ResNet backbone, we observe that training on our well-aligned dataset achieves an AP of 99.36% vs. 91.02% achieved using the dataset provided by Corvi, on images generated by the Latent Consistency Model.
3. **Robustness to additional post-processing operations**: We study the sensitivity of our detector to blurring, additive noise and JPEG compression in Appendix A.2.5. Experiments show that training on our well-aligned dataset demonstrates increased robustness to blurring compared to training on the dataset provided by Corvi.
4. We have also properly labeled the Figure in page 10, and referenced it in the main paper.

For convenience, we have used red, to highlight the text which has been changed/added to the original version in our new version.

Additionally, we address several of the reviewer specific concerns below.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732246049736

thread_type:
public_comment

<thread_object>
creator: ~xiaoxuan_yao1

<content>
title:
Questions About the Motivation and Innovation of the Proposed Method

comment:
This paper focuses on the effectiveness of dataset alignment in fake image detection. Motivated by controlling the differences of the real and fake images (e.g., resolution, semantic content and color tone) can help the detector pay more attention to subtle artifacts introduced by LDM, the authors propose a method that reconstructs the real images using LDM’s autoencoder. Reconstructed images will be treated as fake images. Then the authors train a detector using the same training recipe as proposed in [1]. However, I have three questions about the motivation and innovation of the proposed method.

1. The idea of reconstructing the real images to preserve the image visual content while leaving the artifacts of the generative model and treating the reconstructions as new fake samples have been proposed in [3][4]. Compared with them, is there any evidence to suggest that reconstruction using LDM’s autoencoder preserves the visual content better?

2. Reconstructing the images using LDM’s autoencoder has been proposed in [2], which is a baseline included in the experiments. Therefore, it is difficult to attribute the performance improvement to dataset alignment since [2] utilizes the same data processing techniques. This improvement is very likely due to the use of the classifier proposed in [1] and the idea mentioned in Q1, considering that [2] just employs a simple threshold-based detection and treats the reconstructions as features of training samples rather than new fake samples.

3. The idea of ensuring that the resolutions of the reconstructions are identical to that of their real counterparts is similar to avoiding the bias introduced by the different resolution of real images and fake images, which has been proposed in [5]. The detrimental impact of resizing on fake image detection has also been mentioned in [1]. However, I highly appreciate the authors’ novel discovery that aligning the resolution of real images and fake images improves detector's robustness to resized images.

Would it be possible for the authors to provide additional clarifications about the motivations and strengths of the proposed methods compared to previous works?

**References**

[1] Corvi, Riccardo, et al. "On the detection of synthetic images generated by diffusion models." ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023.

[2] Ricker, Jonas, Denis Lukovnikov, and Asja Fischer. "AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.

[3] Chen, Baoying, et al. "DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images." Forty-first International Conference on Machine Learning.

[4] Yu, Xiao, et al. "SemGIR: Semantic-Guided Image Regeneration Based Method for AI-generated Image Detection and Attribution." Proceedings of the 32nd ACM International Conference on Multimedia. 2024.

[5] Grommelt, Patrick, et al. "Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets." arXiv preprint arXiv:2403.17608 (2024).
</content>
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</thread>
<thread>
creation date:
1733238268273

thread_type:
public_comment

<thread_object>
creator: ~dan_oneata2

<content>
title:
Implications and further references

comment:
This paper provides a sensible way of avoiding shortcuts that are inadvertently introduced when creating fakes; for example, silence ([Müller et al., 2021](https://arxiv.org/pdf/2106.12914)), bitrate information ([Borzì et al., 2022](https://openaccess.thecvf.com/content/CVPR2022W/WMF/papers/Borzi_Is_Synthetic_Voice_Detection_Research_Going_Into_the_Right_Direction_CVPRW_2022_paper.pdf)), preprocessing artefacts ([Chai et al., 2020](https://arxiv.org/pdf/2008.10588)).

**Implications.** However, the main implication of the propsed approach is that the detection model will exclusively focus on the fingerprint of the chosen generative model.
This means that the detection method:
- ... cannot localise inpainted regions by LDM-based methods; as discussed in ([Mareen et al., 2024](https://arxiv.org/pdf/2407.11566); [Smeu et al., 2024](https://arxiv.org/pdf/2409.08849)).
- ... will mark benign manipulations (such as superresolution or image enhancement) as fakes.
- ... will be susceptible to laundering ([Mandelli et al., 2024](https://arxiv.org/pdf/2407.10736)): take a compromising image, pass it through the LDM encoder-decoder and claim that it was fake.

Maybe a discussion on the implications of focusing on the fingerprint (and disregarding the semantics) is useful?

**Further references.** Some other papers that are related and may be worth citing:

- [Chen et al. (2024)](https://openreview.net/pdf?id=oRLwyayrh1) also use reconstruction through diffusion models, although they reproject through the entire model (not only the VAE) and they also reproject the fake samples. (Edit: I now see that this paper was also mentioned in the comment of [Xiaoxuan Yao](https://openreview.net/forum?id=doBkiqESYq&noteId=XsrslAurGB), so sorry for the duplicate).
- In the audio domain, passing the real audio files through the vocoders is a very similar approach and fairly common ([Wang and Yamagishi, 2023](https://arxiv.org/pdf/2210.10570); [Sun et al., 2023](https://openaccess.thecvf.com/content/CVPR2023W/WMF/papers/Sun_AI-Synthesized_Voice_Detection_Using_Neural_Vocoder_Artifacts_CVPRW_2023_paper.pdf); [Wang and Yamagishi, 2024](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10446331)).

---

References:

- Borzì, S., Giudice, O., Stanco, F., & Allegra, D. (2022). Is synthetic voice detection research going into the right direction?. CVPRW.
- Chai, L., Bau, D., Lim, S. N., & Isola, P. (2020). What makes fake images detectable? understanding properties that generalize. ECCV.
- Chen, B., Zeng, J., Yang, J., & Yang, R. DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images. ICML.
- Mandelli, S., Bestagini, P., & Tubaro, S. (2024). When Synthetic Traces Hide Real Content: Analysis of Stable Diffusion Image Laundering. WIFS.
- Mareen, H., Karageorgiou, D., Van Wallendael, G., Lambert, P., & Papadopoulos, S. (2024). TGIF: Text-Guided Inpainting Forgery Dataset. WIFS.
- Müller, N. M., Dieckmann, F., Czempin, P., Canals, R., Böttinger, K., & Williams, J. (2021). Speech is silver, silence is golden: What do ASVspoof-trained models really learn?. arXiv preprint arXiv:2106.12914.
- Smeu, S., Oneata, E., & Oneata, D. (2024). DeCLIP: Decoding CLIP representations for deepfake localization. WACV.
- Sun, C., Jia, S., Hou, S., & Lyu, S. (2023). Ai-synthesized voice detection using neural vocoder artifacts. CVPRW.
- Wang, X., & Yamagishi, J. (2023). Spoofed training data for speech spoofing countermeasure can be efficiently created using neural vocoders. ICASSP.
- Wang, X., & Yamagishi, J. (2024). Can large-scale vocoded spoofed data improve speech spoofing countermeasure with a self-supervised front end?. ICASSP.
</content>
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</thread>


Metareview:
metareview:
Summary. 
This paper aims to detect fake images by improving the aspect of data alignment. The paper reconstructs all real images using the autoencoder of a latent diffusion model and then trains fake image detectors to distinguish between real and reconstructed images. The paper claims that this approach prevents the fake image detector from focusing on unwanted artifacts such as semantic content, resolution, or file format. 

Strengths. 
The paper proposes a simple and efficient way to collect data for fake image detection.
The paper is well-written and has a clear research motivation. 

Weaknesses. 
Proposed method disregards semantic content as a result could be vulnerable to artifact decimation. 
The proposed method uses a detector for a specific decoder (specifically based on latent diffusion models). The generalization of such a detector is unclear. 
The proposed method seems to be sensitive to different transformations. 
The alignment issue highlighted here is not entirely new.

The following paper also uses reconstructions to train a detector. Authors argued that the motivations are different, but I see a strong similarity. In fact, the Chen et al. paper is more focused and accurately represents the detection of diffusion-generated images, whereas this paper (while restricted to latent diffusion models) presented a similar method without qualifying its limitations. 

Chen, Baoying, et al. "DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images." Forty-first International Conference on Machine Learning.


Missing. 
It is unclear if the proposed method can detect fake images from any arbitrary generator or is it restricted to one (or a small subset) of generative models that have similar artifacts as the trained decoder.  

Reasons for reject. 
The idea of dataset alignment is discussed in other papers (e.g., Chen et al. and other papers mentioned by reviewer hbmp). The arguments authors provided on differences are not very convincing. 
It is unclear if the proposed method generalizes to arbitrary generators or latent models with autoencoders that substantially differ from the one used in training. Detailed discussion and analysis are warranted to accept the claims in the paper.

Reasons for accept. 
All the reviewers are leaning toward accept despite their reservations about the claims and scope of the paper.