Venue: NeurIPS 2021 Track Datasets_and_Benchmarks Round1

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: summary and contributions: The paper presents a benchmark for detecting fake (synthetically generated) audio clips. The dataset is derived from a combination of LJSpeech and JSUT, on which several GANs are trained to obtain synthetic samples. The paper also presents an overview of audio representations, techniques for generating audio, baseline results for detecting fake audio, and analysis of transferability across different GANs. The results indicate that the problem is challenging --- no single model is able to achieve good generalization across different generative models. There is also analysis of which representations work well for this task (e.g., LFCC is better and the choice of filter banks). 
strengths: The problem of audio fake detection is challenging and interesting. 

The authors present a variety of experiments with different GAN archiectures analyzing their performance on synthetically generated data from the same and different GANs. 


weaknesses: The dataset is fairly small and is primarily based on LJSpeech dataset, which consists of short audio clips narrated by a single speaker. It is not clear if the methods trained to do well on this benchmark would generalize to novel speakers, or ones with different audio characteristics. Some discussion of the strengths and weaknesses to the proposed benchmark is warranted. For example, how does the method compare to training a model on large-scale synthetic datasets released through competitions such as AVSspoof challenge (https://www.asvspoof.org/)?

There is a concern that the model might learn GAN specific models. One possibility would be to try a leave-one-out validation where a model is trained on all the GAN except one and evaluated on the remaining one.
correctness: The approach appears to be correct. It trains a number of existing GANs on publicly available datasets. 
clarity: Yes. I appreciated the overview of audio representation not being an expert in this domain.
relation to prior work: The paper describes the related work in vision for deep fake detection and existing benchmarks for the tasks. However, it could better motivate the design choices for this dataset. For example, GANs for generating synthetic images typically require a large dataset for realism. Often these could be millions of images of a narrow domain such as faces. Does this limit the sorts of analysis one can do with GANs trained on small datasets? Or are audio GAN techniques fundamentally easier than vision? 
additional feedback: None.
review_title: Deep fake audio detection dataset and some analysis

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: In this paper, the authors introduce a novel dataset of generated audio files, created in order to aid the recognition of deepfakes.  Beginning with two existing repositories of audio clips, "phrases from non-fiction books (LJSPEECH)" and "everyday conversational Japanese 24 (JSUT)", the authors extract Mel spectrograms from each clip, then feed the spectrogram to four models: MelGAN, Multi-band MelGAN, Parallel WaveGAN, and WaveGlow, producing 88,600 generated audio clips in total. They also implement two classifiers as baseline models for future research. 
strengths: The authors present the first dataset of generated audio, to be used in the training of DeepFake detectors.  The dataset represents a timely contribution to an under-addressed area.  There is clear value to researchers in having such a dataset in order to hone detection techniques and learn more about the features of deepfaked audio created by different architectures, even in advance of the wide spread of deepfaked audio. The contribution also includes other aspects of the detector pipeline helpful for researchers' future evaluation of their contributions, such as baseline detectors.
weaknesses: As the authors note in section 5, Discussion, real world data such as data generated by bad actors is hard to come by.  The authors thus focus on generating proxy data and argue that performance on similar tasks for images transfers.  While this all seems correct, I wonder if the authors' task of "recreating" the datasets on which they trained, producing very similar but deepfaked audio clips, means that performance on their dataset will be less likely to transfer.  Some of the primary malicious use cases for deep fakes center on generating novel phrases and sentences -- e.g., creating a clip that resembles a specific person saying something they have never said, such as the authors mention of a case in which a generated voice recording was used to impersonate a specific CEO.  If the CEO never said those phrases, the task of generating audio in that sounds like his voice is more difficult.  Does that make detection easier, and thus transfer of performance more likely? It would be helpful to address this.

It would also be helpful to provide more analysis of features of the dataset itself, in addition to the analyses of the performance of the detectors.
correctness: The authors' claims about the dataset are appropriate and appear correct.
clarity: The paper overall is clear, but could be organized to make its content more readable and better reach its target audience.  I take it that the paper's audience includes the broader NeurIPS community and is not limited to experts in signal processing.  If part of the paper's aim is to encourage research on deepfake audio detection, this makes sense -- a natural audience would be researchers skilled at detecting deepfaked images or video who have not yet tackled audio alone.

Given that, the paper's introduction of basic signal processing terminology in section 2.1 is especially valuable and could be better situated in the flow of the paper.  It would also be helpful to be more consistent with the level of perceived background knowledge expected.  For example, the paper explains Mel spectrograms but not vocoders.  While the paper is of course not responsible for conveying the content of a graduate seminar on signal processing, more could be done to briefly situate the terms used and enable researchers unfamiliar with the field to hit the ground running.

It would also be helpful to distinguish more clearly in the text of the paper between the dataset on which you trained (the original dataset) and the dataset you created (the novel dataset), as this is a source of some sentence-level confusion.
relation to prior work: The authors attest that their paper provides the first (publicly released) dataset of deepfaked audio.  This is correct, as far as I can tell, although I await the verdict of other reviewers, and makes a strong case for its novelty.

On additional factor would be helpful in judging its novelty -- more context for the relationship to the ASVspoof datasets. For example, the authors note that ASVspoof introduced a deepfake audio track as part of the contest in 2021, although did not provide training data.  That provides a clear niche for this dataset's existence (good!) but also prompts the question: how well did the best, contest-winning detectors do on the deepfake audio challenge, even without the benefit of a tailored dataset? How does this compare to detectors trained on this dataset? An answer to that question would make the value of the dataset more clear. 
additional feedback: Overall this is a novel and helpful paper which deserves consideration. I commend the authors.
review_title: Review of WaveFake

final_score: rating: 5: Marginally below acceptance threshold
review_content: summary and contributions: This work introduces a new audio dataset that consists of 88,600 audio clips generated from five different generative models. In experiments, two Gaussian Mixture Models (GMMs) trained separately on the real data and the introduced data show the difference of various generative models. 

strengths: 1. The study of detecting “deepfakes” on the audio/speech domain is timely and can add value to the broader research community.

2. The introduction of an attribution method to reveal that different generative models operate on the low or high frequency looks interesting.

weaknesses: 1. Though the paper claimed to discover the subtle differences between different models (such as operating on lower or higher frequencies), it’s still unclear to me: how the proposed dataset significantly helps detect “deepfakes” on the audio/speech domain. Wouldn’t a dataset that helps detect “deepfakes” be very similar to the real data such that current classifiers cannot discriminate? However, the reported EER looks very large on MFCC. 

2. The way of composing a dataset from an ensemble of current generative models does not sound novel to me, as many prior works have widely used it to collect data (in other domains). 

correctness: The claim in this work is not well supported by the experiments. See the Weaknesses for details.
clarity: The paper is fairly well written, though there are many typos. For example, “Chintha et al.combined” (line 135), “generated with different based methods” (line 128), “The rows shows” (line 253) etc. Also for Eq. (6) , What is c here? Is N here the same as the window size of a frame?

relation to prior work: It clearly discussed how this work differs from previous contributions.
additional feedback: N/A
review_title: It’s unclear to me that the proposed dataset can really help audio deepfake detection. 

Meta-review: 
This paper presents a novel dataset for audio deepfake detection, consisting of clips generated from 5 different generative models. 

The reviewers note that the paper would be strengthened -- especially given the focus of this track -- by a more rigorous analysis of the features of the dataset itself and include more in depth documentation of the dataset following the Datasheets rubric. The work would also be strengthened by a deeper engagement with the ethical dimensions of this work. The authors have addressed some of these concerns in their revised manuscript, including a more comprehensive Datasheet and expanding on the ethical dimensions of the work.

Overall reviewers are still split and there is not a clear champion for the paper so I recommend the authors take the time before the next round of submissions for this track to (a) expand on the work to more deeply connect it to real-world detection of deepfakes and (b) include more analysis of the dataset itself. Such additions would address some of the concerns raised by the reviewers that the papers claims are not well supported by empirical evidence. 

Decision: Reject