TSFF-Net: A deep fake video detection model based on two-stream feature domain fusion

Hangchuan Zhang; Caiping Hu; Shiyu Min; Hui Sui; Guola Zhou

doi:10.1371/journal.pone.0311366

Peer Review History

Original SubmissionJune 14, 2024
1 Sep 2024 Decision Letter - Feng Ding, Editor PONE-D-24-22578TSFF-Net: A Deep Fake Video Detection Model Based on Two-Stream Feature Domain FusionPLOS ONE Dear Dr. Hu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Oct 16 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf. 5. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information. Additional Editor Comments : Please carefully revise the manuscript to solve all the issues raised by reviewers. Also note that, it is not necessary to cite the papers mentioned by the reviewers unless you think it could be academically beneficial for your paper. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The paper presents a novel deep forgery video detection model called Two-Stream Feature Domain Fusion Network (TSFF-Net), which aims to improve deep forgery detection in complex scenes and low-quality images. The experimental results are impressive, showing the high detection accuracy of the method on the FaceForensics++ dataset. But there are still some details worth discussing: 1. Many of the citations had problems showing up as 'Error! Reference source not found', such as the citations in the INTRODUCTION section for the datasets FaceForensics++ and Celeb-DF. 2.The proposed method uses efficient-B0 as backbone, has it tried to use other cnn structures such as ResNet or DenseNet? 3.The goal of the proposed method is to detect the latest emergence of Gan-based face forgery, but the datasets FaceForensics++ and Celeb-DF both seem to be based on encoder-decoder structures, not traditional GAN. hopefully the article has a discussion on gan-generated deepfake, for example, [1][2][3] [1] Synthesizing black-box anti-forensics deepfakes with high visual quality [2] Adversarial attack on fake-faces detectors under white and black box scenarios [3] Securing facial bioinformation by eliminating adversarial perturbations Reviewer #2: In this paper, the authors introduce a novel deep face forgery video detection model named Two-Stream Feature Domain Fusion Network (TSFF-Net), which enhances the model's ability to detect low-quality deepfake videos. Experimental results demonstrate the superiority of this method. In general, the idea of this paper technically makes sense; and the manuscript is easy to follow. However, the following issues should be addressed: 1. The introduction section should further compare the recent research work, especially the research based on transformer and hybrid models, such as RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment. 2. Identify and list research gaps in the related work section. Discuss how the proposed approach fills these gaps and offers advantages. 3. The model structure diagram in Figure 1 can further refine the specific structure of each branch. 4. Include a more detailed discussion of model training information, such as loss functions, hyperparameter optimization procedures, etc. 5. Provide details of the image dataset and training parameters in tabular form. ****** 6. 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Revision 1
11 Sep 2024 Author Response Original Manuscript ID: PONE-D-24-22578 Original Article Title: “TSFF-Net: A Deep Fake Video Detection Model Based on Two-Stream Feature Domain Fusion” To: Editor Re: Response to reviewers Dear Editor, Thanks for your letter and review work on our manuscript entitled “TSFF-Net: A Deep Fake Video Detection Model Based on Two-Stream Feature Domain Fusion”. We greatly appreciate the reviewers’ complimentary comments and suggestions. These comments are very helpful and valuable for improving and polishing our paper. The point-by-point responses to reviewers’ comments are given in the following part. We have added additional information about the datasets in the Methods section and included a statement confirming that our data collection and analysis methods comply with the terms and conditions set by the data sources. We confirm that our submission contains all the raw data required to replicate the results of the study, in accordance with PLOS's definition of the minimal data set. Specifically, the code used in this study is publicly available at: https://github.com/hwZHc/TSFF-Net.git, and all relevant data, metadata, and methods are provided within the manuscript and its Supporting Information files. This ensures that all study findings can be fully replicated. Best regards, Hangchuan Zhang, Caiping Hu, Shiyu Min, Hui Sui and Guola Zhou Reviews and Responses Reviewer #1: Point 1: Many of the citations had problems showing up as 'Error! Reference source not found', such as the citations in the INTRODUCTION section for the datasets FaceForensics++ and Celeb-DF. Response 1: In response to the "Error! Reference source not found" issue you mentioned, we have conducted a thorough examination and correction. We found that the problem occurred in the citation formatting of the reference literature, especially regarding the FaceForensics++ and Celeb-DF datasets mentioned in the "Introduction" section. We failed to format these citations correctly, which resulted in the citations not being displayed properly in the final document. Therefore, we rechecked the format of all citations to ensure they comply with the journal's citation standards. Particularly for the FaceForensics++ and Celeb-DF datasets, we have ensured the correctness of the citation format. Point 2: The proposed method uses efficient-B0 as backbone, has it tried to use other cnn structures such as ResNet or DenseNet? Response 2: Thank you for your insightful comment. In this study, we chose EfficientNet-B0 as the backbone network primarily because it offers a good balance between performance and efficiency. However, we did consider using other CNN architectures, such as ResNet and DenseNet. In preliminary experiments, we tested ResNet and DenseNet but found that their computational complexity was higher, leading to significantly increased training times. Although they could potentially provide slightly higher accuracy in certain cases, we believe that EfficientNet-B0 offers a better trade-off between accuracy and efficiency. That being said, this does not mean that ResNet or DenseNet cannot be used for this task. In future research, we may further explore these or other more complex network architectures to enhance the model's performance. Point 3: The goal of the proposed method is to detect the latest emergence of Gan-based face forgery, but the datasets FaceForensics++ and Celeb-DF both seem to be based on encoder-decoder structures, not traditional GAN. hopefully the article has a discussion on gan-generated deepfake, for example, [1][2][3]. [1] Synthesizing black-box anti-forensics deepfakes with high visual quality [2] Adversarial attack on fake-faces detectors under white and black box scenarios [3] Securing facial bioinformation by eliminating adversarial perturbations Response 3: We appreciate your concern that while our datasets, FaceForensics++ and Celeb-DF, are based on encoder-decoder architectures, they do not represent the traditional GAN structures. We have chosen these datasets because they provide a diverse and high-quality set of deepfake samples, which is essential for assessing the performance of detection algorithms. Regarding the detection of GAN-generated deepfakes, we have included a discussion on this topic in Related works of our manuscript. Reviewer #2: Point 1: The introduction section should further compare the recent research work, especially the research based on transformer and hybrid models, such as RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment. Response 1: We appreciate your suggestion to compare recent research work, we have discussed these models in detail in the Related Work section, we will revise the Introduction section to briefly introduce these models and provide a concise comparison with our research. Point 2: Identify and list research gaps in the related work section. Discuss how the proposed approach fills these gaps and offers advantages. Response 2: We have added relevant descriptions in section 2.2. At the end of Section 2.2, we elaborate on the shortcomings of existing research, which tends to focus more on capturing local features. However, this often overlooks the differences between genuine and forged areas in forged videos. Therefore, we combine facial image features in both spatial and frequency domains, using an improved Vision Transformer (ViT) and cross-attention mechanism for global information modeling, to achieve deepfake detection and enhance the model's performance in scenarios with low quality and high compression rates. Point 3: The model structure diagram in Figure 1 can further refine the specific structure of each branch. Response 3: Thank you for your suggestion. We acknowledge that the model structure diagram (Figure 1) could provide clearer information by further refining the specific structure of each branch. In the revised version, we will update Figure 1 to illustrate the detailed structure of each branch. This will help to better visualize the proposed model architecture and the role of each branch. Point 4: Include a more detailed discussion of model training information, such as loss functions, hyperparameter optimization procedures, etc. Response 4: We have added relevant descriptions in section 4.2. Point 5: Provide details of the image dataset and training parameters in tabular form. Response 5: Thank you for your question. Below is a table that provides details of the image dataset used and the training parameters: Table 1. Basic information of the dataset used. Dataset Real(video) Fake (video) Real(Frame) Fake(Frame) FaceForensics++ 1,000 4,000 509.9k 2,039.6k Celeb-DF 590 5,639 225.4k 2,116.8k Table 2. Details of the training parameters. Training Parameters Settings Optimizer SGD Image Resolution 256x256 pixels Learning Rate 0.01 Weight Decay 0.000001 Batch Size 32 Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0311366.r002
17 Sep 2024 Decision Letter - Feng Ding, Editor TSFF-Net: A Deep Fake Video Detection Model Based on Two-Stream Feature Domain Fusion PONE-D-24-22578R1 Dear Dr. Hu, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Feng Ding Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: (No Response) ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: (No Response) ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: (No Response) ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: (No Response) ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: (No Response) Reviewer #2: (No Response) ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: All my questions were answered. Reviewer #2: For Recommendation 1, it is necessary to add new literature and train-related literature in the references. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0311366.r003
Formally Accepted
20 Sep 2024 Acceptance Letter - Feng Ding, Editor PONE-D-24-22578R1 PLOS ONE Dear Dr. Hu, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Feng Ding Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0311366.r004

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