ILLUSION: Unveiling Truth with a Comprehensive Multi-Modal, Multi-Lingual Deepfake Dataset
Authors: Kartik Thakral
Rishabh Ranjan
Akanksha Singh
Akshat Jain
Mayank Vatsa
Richa Singh
Venue: ICLR 2025 Poster (2025)
Keywords: Multi-modal
Multi-Lingual
Deepfake
AIGC
Primary area: datasets and benchmarks
TL;DR: This paper presents ILLUSION, an unprecedented large-scale, multi-modal, and multi-lingual deepfake dataset with over a million samples. It aims at development of robust deepfake detection systems, closely simulating real-world scenarios.

Abstract: The proliferation of deepfakes and AI-generated content has led to a surge in media forgeries and misinformation, necessitating robust detection systems. However, current datasets lack diversity across modalities, languages, and real-world scenarios. To address this gap, we present ILLUSION (Integration of Life-Like Unique Synthetic Identities and Objects from Neural Networks), a large-scale, multi-modal
deepfake dataset comprising 1.3 million samples spanning audio-visual forgeries, 26 languages, challenging noisy environments, and various manipulation protocols. Generated using 28 state-of-the-art generative techniques, ILLUSION includes
faceswaps, audio spoofing, synchronized audio-video manipulations, and synthetic media while ensuring a balanced representation of gender and skin tone for unbiased evaluation. Using Jaccard Index and UpSet plot analysis, we demonstrate ILLUSION’s distinctiveness and minimal overlap with existing datasets, emphasizing its novel generative coverage. We benchmarked image, audio, video, and multi-modal detection models, revealing key challenges such as performance degradation in multilingual and multi-modal contexts, vulnerability to real-world distortions, and limited generalization to zero-day attacks. By bridging synthetic and real-world complexities, ILLUSION provides a challenging yet essential platform for advancing deepfake detection research. The dataset is publicly available at https://www.iab-rubric.org/illusion-database.

Published as a conference paper at ICLR 2025
ILLUSION: UNVEILING TRUTH WITH A COMPRE- HENSIVE MULTI-MODAL MULTI-LINGUAL DEEPFAKE DATASET
Kartik Thakral Rishabb Ranjan" Akanksha Singh 1.2, Akshat Jain', Mayank Vatsa', and Richa Singh' IIT Jodhpur, India, PISERR Bhopal, India
ABSTRACT
The proliferation of deepfakes and Al-generated content has led to a surge in media forgeries and misinformation, necessitating robust detection systems. However, current datasets lack diversity across modalities, languages, and real-world scenar- ios. To address this gap, we present ILLUSION (Integration of Life-Like Unique Synthetic Identities and Objects from Neural Networks), a large-scale, multi-modal deepfake dataset comprising 1.3 million samples spanning audio-visee visual forgeries, 26 languages, challenging noisy environments, and various manipulation protocols. Generated using 28 sfate-of-thh-arr generative techniques, ILLUSION includes faceswaps, audio spoofing, synchronized audio-video manipulations, and syn- thetic media while ensuring balanced representation gender and skin tone for unbiased evaluation. Using Jaccard Index and UpSet plot analysis, we demon- strate ILLUSION's distinctiveness and minimal overlap with existing datasets, emphasizing its novel generative coverage. We benchmarked image, audio, video, and multi-moda detection models, revealing key challenges such as performance degradation in multilingual and multi-modal contexts, vulnerability real-world distortions, and limited generalization to zero-day attacks. By bridging synthetic and real-world complexities, ILL.USION provides challenging yet essential plat- form for ad advancing deepfake detection research. The dataset is publicly available at phttps: www. iab rubric
dat abase
INTRODUCTION
Detaent Mudiity
The emergence of social media platforms has funda- mentally transformed our mode of communication and information dissemination. Platforms such as Facebook, YouTube, Instagram, and Tokk which boast billions of users worldwide, have expanded the scope of shared content beyond text to include im- ages, videos, and other forms of multimedia Narayan et al. (2022a); Ranjan et al. (2024b). This shift has precipitated surge in the volume of multi-modal content accessible online. As social networks evolve rapidly, they have emerged as the primary conduit for disseminating user- bher-generated multi-modal con- tent. The data circulating on these social networks is predominantly multi-modal, encompassing videos, audio, and images. With their billions-strong user base, these platforms generate enormous data every minute. Nonetheless, the rise of social media and multi-modal content has concurrently fueled an upsurge in the spread of deepfakes and synthetic media fabricated by deep learning techniques. The advancements in generative techniques like Generative Adversarial Networks (GANs). Variational Auto-Encoders (VAEs), and diffusion- basee models, have significantly enhanced the realism of
Unlloddd Vole
Figure/ 1: Comparative analysis of the pro- posed dataset with existing ones based on modalities, size, and manipulations.
"Equal Contributions.

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Published as a conference paper at ICLR 2025
MAFA
Faces Mar
MAAAA
AAAA AAAAA
AAFAA.
Figure 2: Visual representation and organization of each subset in the proposed ILLUSION dataset.
synthetically generated content, making it more convincing to the untrained eye Nirkin et al. (209);
Thakral et al. (2024); Korshunova et al. (2017); )0 |Rombach et al. (202);; Ranjan et al. (2023b). These Generative AI (GenAl) models have exhibited unparal- leled competence in comprehending and generating human-like videos, images, and sounds (Ranjan) et al. 2024a).. In 2023, 95,820 deepfake videos were circulated online, which is 550% increase from 2019 (Deepfake Statistics). Projections indicate that by 2026, up to 90% of online content may be synthetically generated (Synthetic Al Stats). This rapid surge highlights the urgent need for robust deepfake detection techniques. In response. numerous researchers have developed methods
AT foundational models and diffusion-basee
specifically targeting identity-basee deepfakes (Afchar al. 2018 Nguyen et al. 2019 Agarwal
et al. 2019 Khalid & Woo, 2020: Thakral et al. 2023] Chhabra et al. 2024 Ranjan et al. 2023a).
The success of deepfake detection depends the quality of the dataset, including diversity in offers
modalities, generation methods, and the realism of samples. Table ooferss a comprehensive overview of public deepfake detection datasets. Early datasets such as DE-TIMIT (Korshunov 2018). Face- Forensics++ (Rössler et al. 2018)) Celeb-DF (Li et al.) 2020)), WildDeepfake (Zi et al. 22220), and DeeperForensics-L00 (Jiang al. 2020) were relatively small and unimodal, while larger datasets like FFIW 10k (Zhou et al. 2021). ). KoDF (Kwon et al. (02T), and DF-Platter (Narayan et al. 2023)
primarily focused on visual manipulations. Unimodal audio-based forgeries were introduced in ASVSpoof (Wang et al.) (020b), WaveFake (Frank & Schönherr (02T). and TIMIT-TTS (Salvi et al.
2023), whereas multi-modal deepfake detection emerged in DFDC (Dolhansky et al. 2020) and FakeeVCeleb (Khalid al. 2021). More recently, large-scale multi-modal datasets such LAV-DF
(Cai et al. 2022) and AV- -V-DeepfakeIM (Cai et al. 2024) introduced audio-vidde video manipulations, though they lacked non non-identity-bet based AIGC.
Existing data sets have mainly focused on a few specific generation techniques. To bridge this gap, introduce I.LUSIO:: Integration of Life-Like Unique Synthetic Identities and Objects from Neural Networks (ILLUSION Leaderboard). a novel multi-modal, multi-lingual deepfake dataset divided across four sets (visualized in Figure 2). Set A is identity forgery dataset with audio-video synchronized. Set B incorporates Al-generated synthetic data covering three media modalities: image, audio, and video. Set C, a test set, includes pool of real-world Al-generated content (AIGC) sampled from different sources and set D includes multi-lingual and multi-modal deepfake samples spanning over 26 different languages. The dataset is prepared with continuous usage of 40 GPUs, accounting for 2000 GB of cumulative memory. With over 800 GBs in size, the dataset contains over 1.3 million samples encompassing the four sets. To the best of our knowledge, this is one of the largest datasets containing vast variability of generation methods, different modalities, multiple languages, and various challenges (refer Figure
The proposed comprehensive dataset provides diverse Al-generated content to serve as valuable asset for research in detecting Al- Al-generated media varying in input modality, generation models, different languages, and content type. To assess and analyze the utility of our dataset, conduct extensive experiments and benchmark using 11 baseline deepfake methods and analyze their performance when tested in different settings. This dataset is presented leaderboard (ILLUSION Leaderboard) for
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Table I: Details of publicty available deepfake datasets.
Datasrt Name
Year
Samples
Real
Imsges
Fike Samples
Audlo
Video
Samples
Total
Generalam Techniqqes
Svapping
Identity
AT Generatal Content
Mull-- Lingual
Al-wape
Lingual
TEXII
Konhuno 2015
2015
640
NSA
NBA
120
560
2
Bosserr sipporr
2019
1000
NSA
N7A
4,000
5000
Celeh by
2020
590
NSA
N/A
5,629
6,229
DFX
Deinrssy 2p2 AXW
3020
236644
N/A
NA
104,00
128,156
X000
2020
50,003
N/A
NIA
10,00
60000
ASVS
(Ymiii in
2021
16,492
N/A
14R.14
NAA
164,600
19
WaveFake
2021
2021
NIA
117.R5
NAA
117,985
TakeAVColre (amiiia 211
2021
500
NIA
500
9000
10,000
TIMII-11S
NIA
M0,00
$0.490
DadPly
NIA
36,431
N/A
33,176
Miljonrayy
Karal Joln
250,000
Namayan
AV-Deepfammi
Zhen Zhenitsge
TWRGMA
800,000
3000000
NA
DeePhy
100
K.90
9,000
TELUSION
905.58
1,71386
25
researchers to test the performance of their algorithms and foster the development of generalizable detection methods. The primary contributions of our work are summarized below:
We introduce multi-modal deepfake dataset developed using 28 GenAl models grounded in GANs, VAEs, Transformers, and Diffusion-bass models, spanning image, audio, and video modalities. This dataset partitioned into four distinct sets.
The dataset encompasses identity manipulations, where forgery can manifest across audio, video, or both. This set is seamlessly synchronized across audio-visual channels and maintains a balance in terms of sex and skin tone.
The dataset also includes Al-generated content (AIGC) produced by various text-to-modality models across image, audio, and video domains. It encompasses subset of entirely synthetic faces. Additionally, the dataset features real-world deepfakes, designed evaluate detection algorithms in practical context that spans multiple modalities and languages.
We benchmark the proposed dataset using state-of-the-ar detection methods and conduct comprehensive analysis of their performance across range of challenging protocols.
THE ILLUSION DATASET
In this paper, we introduce ILLUSION: Integration of Life Like Unique Synthetic Identities and Objects from Neural networks, comprehensive large-scale multi-modal deepfake datase The dataset comprises 1,376,371 samples across image, audio, video, and synchronized audio-video modalities, making it the largest multi-moda resource available in the deepfake literature. The dataset is organized into four subsets: Set A, Set B, Set C, and Set D. Set A includes identity manipulations featuring faceswaps, voice spoofs, and both. Set B comprises synthetically generated media, including images and videos of sceneries, objects, situations, and music audio. This set also incorporates synthetic faces generated from the website (TPDNE). Set C encompasses real-world testing samples, i.e., Fakes in the Wild, generated using proprietary generative models, and Set D is multi-lingual modal deepfake testing set. ILLUSION is produced using 28 distinct generative models, encompassing both open-source and proprietary systems. Unlike many publicly available deepfake datasets that suffer from imbalances in sex and skin tone (Nadimpalli Rattani 20022] Xu et al. (2022a), the ILLUSION dataset ensures equitable representation across these subgroups. An overview of the dataset is provided in Figure
2.1 DATASET STATISTICS AND ORGANIZATION
This section discusses the statistics and organization of each of the proposed ILLUSION dataset. Table 2 presenns the set-wise statistics.
Set A: This set comprises a total of 13 identity manipulations, generated from 200 unique identities sampled from the CelebV-Text dataset Yu et al. 2023). Each audio, video, and audio-synchronized
'The collection and generation of the dataset have heen approved by the Institutional Ethics Review Commit- Access will be limited to academic institutions for research purposes.
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LANDIAAGE To Semtraatttt AMD pora
<2
48
Eigiallagege
degporiing
IDENUITY SWAPPING
uxly person the frome burkgrrendt
Rasedio inughhal of video p:t0
Righting
kaytk oaly
rideod Seglich
bokkg pcancc Fraate /idee carta sauges stlge 7V0ce
and
Figure 3: Illustration of generation pipeline employed in set A for identity swaps.
clip in this set is 20 seconds long. The samples in this set incorporate variations such occlusions (e.g.. hats, glasses, beards, etc.), body movements, and lighting conditions, thereby ensuring a diversity of variations in the dataset. This set is categorized into four classes: (i) Real Audio- Real Video (RA-RV), (ii) Real Audio and Fake Video (RA-FV), (iii) Fake Audio and Real Video (FA-RV), and (iv) Fake Audio and Fake Video (FA-FV) (as visualized in Figure 3 (abbea]summarize the number of samples available in each class of Set A. Manipulations in images and videos for each of these classes are introduced by swapping the faces of the source identity onto the target video. For this purpose, we employ 6 different face-swapping models, namely, MobileFaceS wap (Xu et al., 20226), FSGAN (Nirkin et al. 2019). FaceShifters (Li et al., (019b). ROOP (Roop) (023). DiffFace (Kim et al. 2022), and DifT' wap (Zhao et al. 2023). The audio deepfakes are created using the English transcription generated through the MMS model (Pratap et al. 2024). This transcription voice clones. We utilize 7 different sendi--generative models, namely, VC (Li ct al. (023). XTTS (Eren & The Coqui TTS)
is then employed by Text-to-Speech systems to create
Team 2021). p. DiffVC (Popov et al., 2021b), DiftHier (hoii et al. (023). YourTTS (Casanova
et al.]
2022). 3. DiffGAN-TTS (Liu et al.] al.] (2022) and GradTTS (Popov et al. (202aa) for voice-swapping
model to ensure seamless synchronization between the audio and video, thereby enhancing the realism of the generated fake. The details of all the models utilized in this are available in the Appendix.
For classes RA-FV, FA-RV, and FV-FV, we employ Alligmment fingerprinting-base
Set B: This set comprises a total of 523,222 entirely synthetic samples and their 138.705 real counterparts, generated through 11 open-source models and one closed-source source model. This set includes images, audio, and videos primarily generated using diffusion models and transformers. For the generation of synthetic im- ages, we employ the images and their corresponding prompts from the training set of the COCO dataset (Lin et al. 2014) to generate using four text-to-image gen- erative models. These models include Stable Diffusion- XL (Podell al. 2023). Kandinsky 2.1 (Razzhigaev ett al. 2023). MultiDiffusion (Bar- Tal et al. 2023), ), and SDXL-Turbe (Sauer et al. 2023). We also collected 8,200 synthetic face images from "This Person Does Not Exist", ensuring a balance in terms of sex and skin tone. These identities are entirely synthetic, have not been swapped, and do not exist in the real world. To generate synthetic audio, we utilize audios and corresponding captions from
Table 2: Dataset Statistics: In Set A, au- dio and images extracted from videos for manipulation, with three compression levels (raw, C23, and C40).
Sets
Modality
Generation Methods
Samples
Real
Samples
Take
Total
Audio
N7A
5,300
5,403
Set A
Images
NIA
403.200
403. 200
Videms
1,032
278,400
279,432
Andio
5,201
20,844
26.055
Set B
Imays
Videns
118,287
7,000
473,148
21,030
$91.435
28,040
Phoes
8,200
8,200
16,400
Set
21,000
21,000
24
D
Andios
3560
41R0
225
1,44,011
1,366,791
the MusicCaps dataset (Agostinelli et al.] (023) and generate 5,211 1 synthetic audio samples each from three text-to-audio generative models and one audio-to-audio model, namely, Audiol.DM (Liu
et al. (023), MusicCien (Copet et al. 2023), MAGNeT (Ziv et al. 2024). and Audio Diffusion) Similarly, we also generated synthetic videos using three tott-oo-iddo generative models, namcly, Text2Video-Zer (Khachatryan et al.) 2023). ModelScope (Wang et al., 2023a). and Zeroscope For this, borrow the corresponding caption for each video from the training set of MSRVTT dataset (Xu et al.] (016) and utilize it to generate 7,010 synthetic videos. The details of all the models utilized in this set are available in the Appendix.
Set C: This set serves as a real-world test set, comprising 21,024 fake samples. It is a curated collection of viral deepfake videos circulated on social media platforms and samples generated using
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Daland
AULOAN IK
Al AV-DeepfakiMM
DP Plater
Malpunazy Kangle
LAKDF
Deaftyy 0.89
TIMEE-TIS 0.00
PakzAYCzkko 0.33
Nat 0009
Kot 6J43
DiffeereFas CU33
DURE: 0.00
Wonefaske 0.00
Jaxsed Index
10:0
cul
o007
0.14
0300
Table 3: Jaccard Index Comparison of ILLUSION with existing deepfake datasets that highlights the uniqueness of the proposed dataset.
proprictary models such as Midjourney and ArtGuru The former includes 24 identity videos. The latter consists of synthetic images generated through a premium API and web interface, respectively. For Midjourney, we utilize prompts from the COCO dataset validation set, and for each prompt, we obtain four corresponding images, resulting in total of 20,000 images. ArtGuru, specializes generating identity-driven images for given prompt. Therefore, we randomly select 1000 prompts from class "person" from COCO dataset to generate total of 1000 images.
Set D: This is a multi- luntillinngal, multi-modal real-world test set containing 4,385 samples curated from social media across 26 languages including French, German, Italian, Chinese, Korean, Arabic, Japanese, Tamil, Kannada, Oriya, Hindi, Sanskrit, Latin, Punjabi, and Gujarati. D.1 with 4,160 web-curated samples and D.2, subset of 225 deepfake samples annotated with four classes (RA-RV, RA-FV, FA-RV, FA-FV), with synchronized audio and video from diverse origins.
divided into
2.2 SIZE AND FORMAT
The ILLUSION dataset is ~800 GB, with Set A clips lasting 20 seconds. All facial images are either synthetically generated or sourced from publicly available datasets with appropriate licensing. Videos are stored in MPEG4.0 format resolution of 512x512, preserving the original frame rate. Consistency is maintained across the dataset through uniform resolution, compression, and generation techniques, with H.264 compression applied at c23 and c40 levels. Skin tones are categorized into four Fitzpatrick scale bins (see Appendix) and combined with two sexes to form eight Seb--gouus-ideenie swaps occur only within these groups. In Set B, videos are generated at 24 frames per second using Text2Video-Zar Zero and ZeroScope. and at 40 frames per second using ModelScope Set C, curated from various sources, varies resolution and length; however, images from MidJourney and ArtGuru consistently maintain resolutions of 1024x 1024 and 512x512, respectively.
2.3 AUDIO AND VISUAL QUALITY ASSESSMENT AND COMPARISON
To assess the visual quality of ILLUSION, we compute the BRISQUE score MMitall et al. 2012 across all four sets (Figure yielding mean score of 38.04 on a (best)- 100 (worst) scale, Table 4 compares BRISQUE scores with Face- Forensies++, CelebDF, DFDC, OpenForensics. and DF-Platter, approximated from (Narayan cct al. 2023). Additionally, we report FAD scores (Kilgour et al. 2018), capturing audio quality, with mean FAD of 9.43. These scores highlight that the ILLUSION is of high quality and is chal- lenging with multiple covariates. Additionaly, we compute the Jaceard Similarity Index to quantify the overlap between ILLUSION and other datasets. As shown in Table 3 the results indicate minimal overlap. with values as low as 0.03 (FakeAVCeleb KoDF) and 0.07 (DF-Platter). Notably, AGIQA-IK. LAV-DE, and DIRE show no overlap (0.00), highlighting I.LUUSION's unique generative diversity.
SR0NGU Scores U1iterrss Detaects
SION with other datasets. Comparing Brisque Score of ILLU-
Figure 4:
2.4 COMPUTATIONAL SETUP
Table 4: Audio and visual quality assessment of ILLU- SION dataset.
In Set A, we utilize a total of genera- tion methods to produce identity-swap across image, audio, video, and audio- video synchronized modalities. This pro- cess is facilitated by Nvidia A100 with 16 GPUs, each with 80GBs of mem- ory. Set B is generated through 11 open-
Dataset Split
Vision (Mean Brisque Score)
Audio (Mean Fréchet Audio Distance)
Train Set
Test Sct
Overall
Train Set
Test Sct
Overall
42.98
48
7.30
29.29
6.64
6.55
Set
NIA
35.66
35.66
N/A
N/A
NIA
Set D.I
NIA
N/A
N/A
N/A
40.39
40.39
D.2
N/A
Overall: 38.64
Overall: 9:43
source and one closed-source generative models, utilizing two Nvidia A40 GPUs, each with 48GBs
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of memory, and three Nvidia DGX stations, each equipped with four V100 GPUs of 32GB memory. Set C comprises samples generated from two proprietary models, produced on 2 Nvidia 3090 GPUs, each with 24 GBs of memory. The benchmarking experiments for the dataset are conducted on 2 A40, each with 48GBs of memory, and 6 A30 GPUs, each with 24GBs of memory, in a multi-GPU setup.
3 EXPERIMENTAL, SETUP
This section outlines the training and testing protocol established for the proposed ILLUSION dataset, followed by a discussion on the deepfake detection methods and evaluation metrics employed for benchmarking. The proposed dataset designed address the following pivotal research questions:
RQI: How effective are the detection systems in detecting multi-modal identity-swaps?
RQ2: How effective are the detection systems in identifying synthetically generated media?
RQ3: How robust and reliable are the current state-of-thh-arr detection algorithms when deployed in real-world scenarios?
RQ4: Is it feasible to detect identity swaps and synthetic media in a zero-day attack setting?
RQ5: Is it possible successfully trace back the source of a given deepfake?
3.1 EVALUATION PROTOCOLS
The ILLUSION dataset is composed of four sets. Sets A and B are partitioned into training and testing subsets in a ratio of 3:1. The training data is split into a 9:1 ratio to divide into train and validation data. To mitigate the skew between the "Real" and "Fake" classes in set A, we borrow an additional 144 videos (18 subjects/sub-group from the CelebV-Text dataset. In contrast, set C and D exclusively
set. For all the videos Set A. we extract 10 frames from each fake video and all from each real video. For Set B, we pick 24 frames each from the generative models and select every sixth frame from real videos. Further, for synthetic images generated from four text-to-image models, we repeat their corresponding real images four times. This approach addresses the imbalance between the dataset's ' real and fake samples.
This protocol utilizes Sets A and B, each with their respective training and test sets. Set divided subject-disjoint manner, incorporating 160 subjects in the training set (20 subjects/sub-grat gubss/ and 40 subjects in the test set (5 subjects/sub- group). The audio, video, and multi-mode deepfake detection models are then trained and tested on the samples from Set A. The results presented in three compression settings raw, C23, and C40- to facilitate the assessment of deepfake quality in the dataset relative to existing datasets (results are shown in the Appendix). For Set B, images borrowed from the COCO dataset (Lin et al., 2014) and BFW dataset (Robinson) 2022) serve as real samples corresponding to the fake samples generated using text-to-image models and synthetic faces, respectively. Similarly, audio and video samples from the MusicCaps dataset (Agostinelli et al. (203)) and the MSRVTT dataset (Xu et al. 2016) are used real samples corresponding to fake samples generated using -ext-to-audio and text-to-video models. We extract 24 frames from fake videos to classify synthetic videos and select every 6th real video frame to maintain data balance.
Protocol 1- Multi-modal Deepfake Detection:
Protocol 2 Zero-shot/Zero-day Generalization: The primary aim of this protocol is to test the generalizability of detections on new or unseen generation methods. The detection models are initially trained on the train set of Set A and subsequently tested on the test set of Set B. The performance of the models is also evaluated a vice-versa setting.
Protocol 3 Generalization mance of existing state-of-the-arr models on real-world deepfake samples. Here, the models are trained on the train set of Set A, Set B, both, and their performance is evaluated on Set C.
Real-World Deepfake Media: This protocol assesses the perfor-
The final protocol presents a challenging model attribution task, i.c. to predict the generative technique used to create the input deepfake. The models are trained and tested on fake samples generated from each technique from Set and Set B. The detection models are evaluated separately for image, video, and audio modalities.
Protocol Performance on Model Attribution:

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Table 5: Classification performance for visual components of the dataset obtained by varying the training and testing sets.
Trained
On
Models
Accuracy
Accuracs-Fale Fake
Set
Aecuraty-Koal
AUC
Accurass
Accuracy-1 oike
Aecuracy:Kcal
AFC
Set CTECTAI Fake)
Accuracy
FINcz
0.851
(175)
(135)
0.5455
0.4455
(14755
0.48))
01462
0::41
Set
MesolnceptionNee
DSP-FWA
0,506
0.984
(1982
0.999
0.956
0.882
0.583
0.997
0.42%
0.505
0.457
0.983
0.418
0.473
0:487
0.396
0,991
0.214
Korption
0.851
0.846
0.856
0.920
0.497
0.749
0.283
0.515
0.633
E:Ne
0,498
01205
01901
0,397
0.981
[1991
01910
0.3915
0.717
Set
DSP FWA
0
0.495
0.009
0.001
0.987
0.999
0
0.586
0.995
0.757
0.553
0.994
0.967
0.990
0.919
0.599
0.727
0.045
FINet 0,502
Necyrion
0.015
0.992
01536
0.977
0,987
0.966
0.991
0.725
Set
DSP EWA
0,972
0.651
0.975
0.926
0.36
0.970
0,995
0.958
0,992
0.956
0.986
0.981
0.989
0.925
0.994
1.000
0,703
0.865
Mesolncoptionneet
0.681
0.013
0.425
0.701
0,834
0.806
0.912
0.948
0.241
Xception
0,851
0.925
0.835
0,954
0,972
0.982
0.963
0.987
0,650
3.2 BENCHMARKING DETAILS
DeepFake Detection Methods We utilize four state-of-the-ar video and four audio deepfake de-
tection models to benchmark all three sets of the proposed dataset. For video deepfake detection.
we employ MesolnceptionNet (Afchar et al., 2018 XceptionNet (Chollet 2017), SWA- (Li & Lyu 2018). and F3Net (Wei et al. (0020). For audio deepfake detection, we use RawGAT-ST (Tak et al. 2021 D. AASIST (Jung et al. 2022). SSLModel (Tak et al. 2022), and Conformer GGulati et al.
2020). We also benchmark the proposed dataset using multi-modal deepfake detection algorithms. Specifically,
employ methods such as MRDF (Zou et al. (024) and FACTOR (Reiss et al. 2023). Additionally, we use an ensemble of F3Net and SSLModel, which are baseline unimodal models (referred to as unimodal ensembling), and report class-wise video-level accuracy. Benchmarking the proposed ILLUSION dataset with 11 baseline algorithms provides a comprehensive evaluation that encompasses both typical methods and type-complete approaches. The unimodal baselines focus modality-speciffe behaviors, enabling a deeper understanding of how state-of-the-art algorithms per- form within their respective domains (e.g., image, audio, or video). In contrast, multimodal baselines evaluate type-complet methods, capturing the interplay between multiple modalities and offering insights into cross-modal generalization and robustness. This dual benchmarking strategy ensures a balanced assessment of both specialized and holistic detection capabilities. Detailed descriptions of all these algorithms are provided in the Appendix.
Evaluation Metries For models trained on image and video data, we provide frame-level accuracy and Area Under the Curve (AUC) scores. Each frame video classified as fake or real. We also present class-wise accuracy for additional analysis. For audio data, we report the Equal Error Rate (EER) and AUC score. For models trained on multi-modal data, such as combined video and audio, we provide video-level accuracy, using a threshold set at 50% of frames to classify video as fake.
Implementation Details The DSFD detector (Li et al. 2019a) is used to extract faces from the frames of videos containing faces. For all protocols, the models are trained for 30 epochs with early stopping, and the models with the best validation accuracy are selected. We use the Adam optimizer with initial learning rate of 0.0001. A batch size of 256 is used for distributed training.
4 RESULTS AND DISCUSSION
This section discusses the benchmark results obtained using the state-of-the-an deepfake detection models mentioned in Section 3.2 when trained and evaluated on the proposed ILLUSION dataset. The performance analyzed is in accordance with the protocols described section 3.1
To analyze the performance of audio and visual detection models, we trained and tested them on both Set A and Set of the proposed II..USION dataset. From Tables 5 and 6 for set A. we observe that all architectures perform well for visual as well as audio detection models, with DSP-FWA achieving the best performance for visual data (99.3% accuracy
Protocol| 1 Multi-Modal Deepfake Detection:
Set B) and SSLModel excelling in audio data with EER of 0.006. A similar trend is visible in set B, where we observe that all the detection models, when trained on synthetic data, are able to achieve a promising detection performance.
We observe significant variability among the models when evaluating performance on Set C-which consists entirely of curated real-worle and compressed deepfakes. For instance, DSP-FWA achieves
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Table 6: Classification performance for audio components of the dataset obtained by varying the training and testing sets.
Trained On
Models
Set
Set B
EER
Aceuracy-Fake
Recuracy-Real
AUC
EER
Accuracy-Fale
Accuracy-Real
AUC
RawGAA-ST
0.443
1,000
0.000
0.576
0.470
0,559
0.004
0.539
Set A
AASIST
0.049
0.879
1.000
0
0.430
0,240
0.922
0.563
Conformer
0.025
0.996
0.975
0.991
0.562
0,715
0.081
0.395
SSLMolel
0.006
0.980
1.000
1,000
0.583
0.676
0.104
0.356
RawGAT-ST
0.381
0.125
1.000
0.699
0.032
0,988
0.939
0,995
Set B
AASIST
0.261
0.071
1.000
0.805
0.020
0.988
0.970
0.997
Conformer
0.821
0.008
0.825
0.119
0.005
0.994
0,999
SSLModel
0.694
0.005
0.925
0,252
0.006
0.993
0.993
0.999
Set A
KawGAT-ST
0.025
0.943
0,954
0.035
0,973
0.938
0,991
AASIST
0.050
0.946
0.950
0.992
0.027
0.983
0.951
0.9%
Conformer
0.030
0,959
0.975
0,995
0,020
0.990
0.959
0.997
Set B
SSLModel
0.069
0.938
0.925
0.988
0.022
0.992
0.952
0.998
only 21.4% accuracy when trained on Set A (synthetic data) and tested on Set C, compared 72.7% accuracy when trained on Set B (real-world data) and tested on Set C. This discrepancy highlights the challenges of generalizing from synthetic training data to real-world deepfake scenarios. Conversely, MesolnceptionNet exhibits unusually high accuracy on Set C due to its tendency to classify most inputs as fake. Since Set C contains only fake samples, this bias results inflated performance metrics for MesoonccetionNee.. Set C was intentionally designed as a challenging test set that closely mimics real-world deepfakes crafted for mass misinformation campaigns. Its inclusion emphasizes the necessity for detection models to handle diverse array of generative techniques and the complexities inherent in worldworld scenarios. This highlights the limitations of current approaches and showcases the forward-lookin design of the ILLUSION dataset in advancing deepfake detection research.
We evaluated the effectiveness of multi-modal deepfake detection methods Set A of the proposed ILLUSION dataset. These methods were trained on audio-video synchronized samples from Set A. The performance achieved are detailed in Table 7 Our findings show that MRDF outperforms FACTOR across all classes, notably achieving an 87.1% classwwise accuracy for the Fade Audii--Faakeeeeee Video category. Conversely, FACTOR consistently underperforms, with notable low of 15.7% accuracy for the Real class. Unimodal Ensembling shows potential, achieving a 77.9% accuracy on the Fake-udio- Video class, but falls short for the Real-AdiioRRelliii class.
Table Classification performance of modtil deepfake detection methods
on set A.
Models
RA-RV
RA-FV
FA-RV
FA-FV
MRDF FACTOR
0.775
0.446
0.827
0.871
0.157
0.352
0.369
0.413
Unimodal
0.208
0.887
0.359
0.779
Ensembling
To assess the impact of noise and neural compression, we evaluate baseline models trained on Sets and B. testing them on the corrupted version of Set C (as shown in Table of the Appendix), Here we observe a significant performance degradation. Additionally, we investigate different compression levels for deepfake detection models, individually training and testing them raw, C23, and C40 compressions of Set A. From Table 2 (Appendix), DSP-FWA consistently performs well across most combinations. While there's drop in performance when models are trained on higher quality and tested on compressed samples, those trained on C23 and C40 exhibit better generalization for both raw and C40 samples.
Protocol 2 Zero-Day Attack Generalization: We evaluate the deepfake detection models on the challenging setting of zero-day attack detection. In this, each model trained on training data from one set of the ILLUSION dataset and is tested on the test data of the other set. For evaluation on unseen attack setting, we train each model on train data of set A and test its performance on test data of set B, and vice-versa. The performance achieved is reported in Table and Table We clearly observe that all the visual and audio detection models, when trained on set data and tested on set data, consistently achieve random performance. The same observation is made for both visual and audio detection models when training data of set B is trained and evaluated on test data of set A. From this, we infer that the artifacts introduced in identity-swaps deepfakes and completely synthetic deepfakes are completely different. Due to this, the detection models trained on one is unable to generalize the other. With this, note that the proposed ILLUSION dataset will provide the researchers with a diverse range of deepfakes to capture variety of artifacts in training for better generalizability in real-world deployment.
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Protocol 3 Generalization on Real- World Deepfake Media: Since set C contains only visual deepfake media, we train the visual deepfake detection models in three different settings and report the accuracy set C in T First, the models are trained on the image and video data of set A and then tested on set C. Then, the models are trained on set B and tested on set C. Finally, we train the models combination of visual data from sets A and B and test it set C. We observe that models perform better on set C when trained on set B than when trained on set A. This behavior is observed because most samples are synthetically generated using lext-to-image models like MidJourney and ArtGuru. Whereas identity swaps are very few. We also observe a slight increase in performance on set C when the detection models are trained on a combination of set A and set B.
We further perform a comprehensive evaluation of audio and multi- modal detection models on Set D, with detailed results presented in Tables X and [9] respecctively. The samples in Set 1) were as- sessed using audio detection models for both subsets D. and D.2. For Set D.I, we employed audio detection models pretrained on a combination of Set A and Set B from the ILLUSION dataset. As shown in Table 8 all architectures encountered significant challenges in generalization, with the conformer model achiev- ing the highest AUC of 0.488. For Set D.2, we utilized MRDF, FACTOR, and Unimodal Ensembling for multi-modal baselining and report the performance in Table ? It is evident that model performance drastically declines when evaluated on Set D, which involves multi-modal real-world fakes. Nonetheless, FACTOR outperformed Unimodal Ensembling.
Table 8: Classification accuracy of audio deepfake detection mod- els (trained on Sets A and B) tested
Set D.I. Set D com- prises audio samples from more than 26 languages.
Models
EFR
AUC
SSLModel
0,578
0.397
Conformer
0,523
0,488
AASIST
0.506
0.471
RawGAT_ST
0,571
0.402
These results highlight the formidable challenge posed by the multi-lingual and multi-modal nature of Set D, revealing that current state-of of-the-art algorithms strug- gle to generalize to real-world deepfakes. Our obser- vations indicate that Laae-oofthhe-rr detection models face significant difficulties when confronted with the complexity of multi-modal and multi-lingual deepfakes prevalent on social media platforms. These findings
Table 9: Classification performance of multi-modal deepfake detection methods on set D.2 of the ILLUSION dataset.
Models
RA-RV
RA-FV
FA-FV
MRDF
0.31
0.10
0.15
0.09
FACTOR
0.35
0.12
0,20
0.13
Unimodal
0,25
0.09
0.11
0.05
Ensembling
highlight the challenges and limitations these models encounter in adapting to the diverse and evolving nature of deepfake content.
Protocol 4- Performance Model Attribution: Dif- ferent generation techniques introduce distinct artifacts in deepfakes (Wang et al.) 2020a; Frank et al.) 2020) Wang et al. 2023b). From Protocol 2, we observe that identity-swap deepfakes exhibit artifacts different from those in fully synthetic media. To analyze detection models' ability to identify the source generation tech- nique, we report their performance in Table 10 All vi- sual models, except MesoInceptionNet, accurately clas- sify iden deepfakes, with DSP-FWA achieving near-perfect accuracy. A similar trend is seen in audio models, where Conformer performs best.
Table 10: Model attribution on Set A
Attribute
Models
Accuracy
AUC
F3Net
0,923
0.933
DSP-FWA
0.987
1.000
Video
Mesooncepiioneer
01444
0.620
Xception
0.880
0.832
RT
0.941
0.995
Audio
AASIST
0.937
0.998
Conformer
0.967
1.999
MSLModel
0.959
0.998
For the model attribution in set B, detection models are evaluated separately for each modality. We report the performance in Table [1] For attribution in text- to-image (including synthetic faces samples) and text- to-video models, DSP-FWA consistently achieves the highest performance with an AUC of 97.8% and 99.9%, respectively. Similarly, for attribution in text-to-audio data, all the detection models are successfully able identify the source of the generation model with comparable performance. From these observations, we note that each generative model introduces unique signatures in their generated output. The detection models pick these signatures for near-perfect performance on model attribution task.
Table 11: Model attribution Set B
Attribute
Models
Accuracy
AUC
F3Net
0.878
0.971
FWA
11911
11978
Images
Mesolncoptionee
0.499
0.822
Xception
0.889
0.972
F3Net
0.994
0.999
FSP
0.998
0.999
Video
MesoInceptionNet
0.909
0,994
Xception
0.996
U.999
RawGAT-ST
0.991
0.999
Audio
AASIST
0.993
0.999
Conformer
0.899
0.998
MoLMl
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5 DISCUSSION AND CONCLUSION
In this paper, we introduce the ILLUSSON dataset, a significant step towards a comprchensive, multi- modal deepfake resource. Created using 28 otate-of-the-art generative models, ILLUSION provides diverse Al-generated content across image, audio, and video modalities, including both curated real-world deepfakes and synthetic media. This design enables models trained on ILLUSION to learn features that extend beyond synthetic artifacts, enhancing generalization across domains, particularly in multilingual and noisy settings. Results show that detection models trained on ILLUSION outperform those trained on existing datasets when evaluated unseen generative techniques and real-world forgeries. Designed to aid the development of robust, multi-modas multilingual detection systems, the ILLUSION dataset reveals several key insights through our analysis:
Multi-Modal Deepfake Detection: The high performance of models like DSP-FWA and AASIST on both visual and audio data suggests that current models are effective detecting deepfakes when trained on data from same distribution. However, the disparity in performance between identity swaps and completely synthetic data indicates that models may be learning to identify artifacts specific to the generation method rather than generalizable features of deepfakes.
The significant drop in performance when models trained one set are tested on another accentuates the challenge of zero-day attack detection. This suggests that models are currently not robust against deepfakes generated by unfamiliar methods, highlighting the need for diverse datasets like ILLUSION.
Zero-Day Attack Generalization:
The subpar performance of models trained on identity-swap and synthetic data, when tested on real-world deepfakes across various languages, depicts the necessity of curated, multi-lingual deepfake dataset. Such a dataset crucial for enabling models to effectively generalize to the diverse deepfakes encountered in the wild.
Generalization on Real-World Multi- Multi-Lingual Deepfake Media:
The ability of models to identify the source generation technique with high accuracy demonstrates that generative models leave distinct signatures in their outputs. This could have implications for the traceability of deepfakes and the accountability of generative model creators.
Model Attribution:
The ILLUSION dataset specifically addresses deepfake detection challenges using specialized gen- erative AI techniques, while acknowledging that generalized forgery methods-such as digital watermarking, image manipulations, and Photoshop-based edits-represent another important aspect of media forensics. These generalized methods, often easier for deep-lcarning-beee -bsed detectors to identify and fundamentally different from generative deepfake techniques, would require additional design considerations to maintain dataset consistency. Moreover, ILLUSSON prioritizes quality and diversity over sheer size, with each subset carefully curated for distinct purposes, such as evaluating generalizability or robustness to compression artifacts. By incorporating 28 distinct generative methods, along with multi- multi-modal, multi-lingual, and real-world samples, the dataset minimizes redundancy, ensuring relevance and providing valuable insights into detection performance under diverse conditions. Future extensions of ILLUSION will explore the integration of generalized forgery methods to broaden its scope and utility.
ETHICS STATEMENT
Our analysis estimates that approximately 245 kg COz-equivalent emitted during the creation of this dataset (ML CO2, Impact). Despite this environmental impact, the societal benefits are significant. ILLUSION offers valuable resource for researchers explore detection methods across diverse types of fake media. Additionally, its balanced representation of gender and skin tone promotes fairness in the development and evaluation of detection techniques. As a comprehensive multi-modal, multi-lingual deepfake dataset, ILLUSION is instrumental in the global fight against misinformation.
ACKNOWLEDGEMENT
This research is supported through grant from MEITY under the National Supercomputing Mission. Thakral received partial support from the PMRF Fellowship and Vatsa is partially supported by the Swarnajayanti Fellowship.
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APPENDIX
A EXTENDED DETAILS OF ILLUSION DATASET
Details of Skin Tone Scales: In this dataset, have adopted the simplified Fitzpatrick Scale. This scale has been reorganized from six categories to four. The details of each category are as follows:
Bin I: Light (Fitzpatrick and II)
Bin 2: Light-Medium (Fitzpatrick IIII
Bin 3: Medium-Dark (Fitzpatrick IV)
Bin 4: Dark (Fitzpatrick V and VI)
O
Figure 5:
Visualization of diverse samples from the proposed ILLUSION dataset, showcasing variations in resolution, quality, and generative techniques across different sets.
Performance Noisy data and Neural Compression: We investigate the robustness of deep learning models against Gaussian and salt-and-pepper noise, as well as neural compression algorithms. To achieve this, we evaluate baseline models trained Sets A and B from the proposed IL..USION dataset. Specifically, we analyze the performance of these models on the noisy and compressed samples from Set C, summarized in Table [2 Our findings reveal that the models face challenges in generalizing effectively when confronted with noisy and neurally compressed data from Set C.
In this research, we examine the influence of varying degrees of compression on the performance of visual deepfake detection models. These models are individually trained and evaluated three distinct versions dataset namely raw, C23, and C40 compressions. Our observations, as detailed in Table 13 reveal that the DSP-FWA model consistently outperforms others across nearly all combinations training and testing conditions. Interestingly, we notice decline in performance when the models are trained on high-quality (raw) data and subsequently tested on compressed samples. This suggests potential challenge in the model's s ability to generalize from high-quality training data to lower-quality testing scenarios.
Performance on Compressed Data:
However, promising results emerge when we consider models trained on C23 and C40 compressions. These models demonstrate enhanced generalization when evaluated on both and C40 samples, underscoring the benefits of training on compressed data. Consequently, this approach improves the robustness and adaptability of deepfake detection models across varying input qualities.
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Table 12: Classification performance different noise and neural compression added to Set C. The models are evaluated on Gaussian noise (with deviations of 0.5 and 0.75), Salt and Pepper Noise (with noise density of 100 and 150), and Neural Compression (Cheng al. (020)) and Minnen et al. (2018). ). The baseline models are trained on Set A and Set B and tested on the noisy version of Set C.
Trained
Gaussian Noise
Salt and Pepper Noise
Neural Compression
Models
On
0.5
0.75
100)
150
Cheng et al.
Minnnen
F3Net
0.612
0.601
0.634
0.622
0.525
00.481
DSP-FWA
0,592
0.571
0.601
0.584
0.516
0.508
Set A
MesolnceptionNet
0.553
0.52
0.538
0.495
0,502
0.497
Xception
0.627
0.605
0.643
0.626
0.582
0.554
F3Net
0.604
0.593
0.627
0,606
0,544
0,504
DSP-FWA
0,582
0.572
0.575
0.572
0.485
0,493
Set B
MesoInceptionNet
0.537
0.544
0.513
0.508
0.498
0,,998
Xception
0.601
0.593
0.612
0.582
0.604
0,582
Table 13: Classification performance for visual components of the dataset for intra and cross- compression of set
Inaind
Rav
C-33
C-48
FINc Mascbssptooeee
8251
0.75
[051
0.365
(5525
0.703
0032)
00037
0.715
0.75a
0.533
0.352
19778
0.915
(0481
2.408
01911
Xaytion
EXS1
0.545
0530
0.20
0536
0.836
0.345
0.95
0.345
0.611
0.34
0.343
03313
1300
0030
0357
0137
C-21
MostesptinNN
6545
0.51
0578
0917
0535
0.755
0.373
0011
0342
0985
0.83
0.825
Kamtaa
0194
0365
01991
0723
0820
0180
11359
C-40
Mosteooptoneer
DSR-FWA
6776
0.53
0190
5.69
0778
0333
0728
0463
0.066
0055
0338
0027
0.51
0851
3169
0.544
0.73
Comparison with Existing Deepfake Datasets: We evaluate ILLUSION's uniqueness and relevance in comparison to existing datasets through series analyses.
We employed the Jaccard Similarity Index, defined the ratio of the intersection to the union of two sets, to quantitatively assess the overlap between the ILLUSION dataset and existing datasets. This metric, ranging from (disjoint sets) to 1 (identical sets). provides measure of set similarity. The results, as summarized in Table 3 demonstrate a substantial lack overlap. This confirms ILLUSION'ss distinctive coverage and unique contribution to the landscape of generative method datasets.
Next, utilized the UpSet plot (visualized in Figure analyze the distribution of generative techniques, which revealed both unique contributions and shared intersections. The largest intersection corresponds to generative techniques exclusive ILLUSION, including diffusion-based algorithms such as DiffSwap. DiffFace, DiffVC, and DifiGAN-TTS, as well as multi-modd modal synthesis methods. These highlight ILLUSION's novel contributions to media forensies. In contrast, smaller intersections with large-scale datasets like ForgeryNet and KoDF represent shared techniques such as GAN-based image synthesis and lip-syne forgeries, ensuring ILL.SSION's benchmarking relevance against state- of-the-art methods. The relatively smaller shared intersections, compared to ILLUSION's unique contributions, highlights the dataset's comprehensive design bridging gaps across modalities while introducing underexplored generative methods.
To directly assess feature differences, we conducted feature embedding analyses using ResNet50 pre-trained ImageNet. Uniformly sampled data (50,000 samples each) from ILLUSION and FakeAVCeleb were compared. The t-SNE plot (visualized Figure reveals distinct clustering patterns with minimal overlap, indicating significant differences in feature distributions. Additionally, the KL divergence of 1.62 between the feature embeddings quantitatively reinforces this dissimilarity. These objective metries affirm that ILLUSION introduces novel and diverse generative techniques the detection ecosystem, complementing existing datasets.
B GENERATION PIPELINE
In this section, we discuss the methods used for the generation of the proposed IL..USION dataset.
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Overiap of Generative Techniques Across Datasets
Intersection size
15
12
Kaggle
2
LNW-DF
AGIQA-1K
DeePhy
DF-Platter
1/-Deepaaee1M
FakeAVCaleb
WaveFake
KoDF
DiffusionFace
TIMIT-TTS
ForgeryNnt
28
ILLUSION
Figure 6: Overlap of Generative Techniques Across Datasets (UpSet Plot) This plot illustrates the overlap of generative techniques across ILLUSION and other publicly available datasets. The largest intersection corresponds to 23 generative techniques unique to ILLUSION, including diffusion-basse methods and advanced multi-modal synthesis techniques. Smaller intersections highlight shared generative methods between ILLUSSON and datasets like ForgeryNet, KODF, and DF-Platter. The plot highlights ILLUSION's distinctiveness while maintaining relevance through its integration of shared techniques.
B.1 SET A: IDENTITY SWAP DEEPFAKES
The process of generating identity swaps of set A is visualized in Figure (of the main paper). To generate these swaps, we utilize the YouTube videos from the CelebV-Text dataset (Li et al. (020). We select 200 unique real identities, ensuring that 25 identities were chosen for each sub-group within the four skin-tone bins (as described in section A and for both sexes, Male and Female, to maintain balance in the dataset in terms of sex and skin-tone group. Fach real video clip is 20 seconds long and includes various occlusions, such as hats, glasses, excessive body movements, and harsh lighting, to ensure diversity in dataset. Furthermore, for Females from the third bin of the Fitzpatrick scale, we scraped 25 videos from YouTube to compensate for the lack of English language videos in Celeb Text and generate 25 images from "This Person Does Not Exist" (TPDNE) for face-swapping. We then assigned one identity from the YouTube video to another identity from the same generated images.
We utilize video generative models, audio generative models, and an audio-vides video synchronization tool. Each of them is discussed below in detail.
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1992
Dar LURON
Figure 7: Feature-Leved Comparison Between ILLUSION and FakeAVCellb (PCA and t-SNE) The PCA (left) and t-SNE (right) plots compare the feature embeddings of 10,000 randomly sampled data points from ILLUSION and FakeAV extracted using ResNet50 model pre-trained on ImageNet. The plots reveal distinct clustering patterns with minimal overlap. indicating significant differences in feature distributions. A KL-divergence of 1.23 further quantifies these differences, validating that ILLLUSOON provides unique feature representations compared to existing datasets.
Visual Generation: For identity swapping i.e., face swapping in videos, 6 different models are employed, namely, MobileFaceSwap (Xu et al. 20226), FSGAN (Nirkin al. 2019), FaceShifters (Li et al. 2019b), ROOP (Roop) 2023). DiffFace (Kim et al. 2022). and DifISwap (Zhao et al. 2023). For all possible combinations within sub-group, the source (from which facial features are extracted) swapped onto the target faces (which provide the face shape and background). Each manipulated video has the same duration as the corresponding real video in the base dataset. Each of the video models used for manipulating faces are discussed below:
FSGAN Nirkin et al. 2019) is Face Swapping GAN model for face swapping and reenactment. It is subject-agnostic and can be applied to pairs of faces without requiring training on those faces. The model for video-to-video is used with default hyperparameters.
FaceShifter (Li et al., 2019b) generates the swapped face, extracting target attributes and embedding them onto the source. FaceShifter is robust against occlusions using two network pipelines, AEI-Net and HEAR-Net, trained to recover anomaly regions self-supervised without manual annotations. The pre-trained weights of ArcFace and AEI-Net are used to extract identity embeddings, and utilize source images and target videos to generate videos.
FobileFaceSwap (Xu et al. 2022b) is lightweight Identity-aware Dynamic Network (IDN) for subject-agnostic face swapping by dynamically adjusting the model parameters according to the identity information. The ArcFace and model pre-traimm trained weights used for identity embedding extraction and inference. We utilize source images and target videos generate videos with default hyperparameters.
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Roop (Roop 2023) is one of the most widely used open- deepfake generation software. Roop uses a face-swapping pre-trained model of InsightFace by Deeplnsight. The default hyperparameters are used for generation while extracting source frame swap onto a target video.
DiffFace (Kim et al., 2022) is a diffusion- based face-swapping framework composed of training III conditional DDPM, sampling with facial guidance, and target- prget-preserving blending. 40 equidistant frames are extracted from videos to create face swap images for the unseen test set. The default hyperparameters and pre-trained weights are utilized.
DiffSwap (Zhao et al., 2023) is diffusion model-based framework for high-fidelity and controllable face swapping. We use the pre-trained weights for face and landmark detection.
Audio Generation: The identity swapping for audio i.e., audio spoofing is performed using 7 different
models, namely, Free (Li et al. 2023). XTTS (Eren & The Coqui TTS Team 202T). DifVVC (Popov et al., (021b), DiffHierVC (Choi et al.
2023). YourTTS (Casanova
ct al. 2022). DifiGAN-TTS (Liu et al. (022), GradTTS (Popov et al.,
2021a) to create this dataset. The audio is extracted from the selected videos, and the MMS model (Pratap et al., 2024) is used to identify the language and English audio is selected. Next, we generate a transcription of the audio using the MMS model. This generated transcription is then used generate synthetic audio. Additionally, the raw audio is used to create voice-cloned audio of the source speaker. We clone voices for all the combinations of source (speech which is cloned) and target (text of the audio borrowed from) within a sub-group. This ensures that the dataset's s quality is maintained and that altered content is realistic by preserving the unique accents within sub-group. Each audio limited to 20 seconds in length to maintain the quality of the synthesized speech without compromising on the audio's s duration. We discuss each of the audio models used for generating audio below:
VC (Li et al., 2023) is an end-to-end model for voice conversion (VC) based on the approach proposed in VITS. The VC model is variational auto-encoder-bait -ased architecture pre-trained on the VCTK speech corpus. We use the model provided by Coqui generate cloned voices using the pre-trained speaker encoder model.
XTTS (Eren & The Coqui TTS Team, 2021) is an end-to-end voice generation model for voice cloning into different languages built on Tortoise. XTTS has significant model changes that simplify cross-language voice cloning and multi-lingual speech generation. Coqui is used to generate clones.
DiffVC (Popoy et al., 2021b) is another zero-shot many-to-many voice conversion method designed for the general case when source and target speakers do not belong the training dataset. The model is trained using HifiGAN vocoder, and for voice conversion, the model is trained on VCTK and LibriTTS speech corpus. We use the pre-trained speaker encoder and voice conversion model trained on VCTK for cloning.
Diff-HierVC (Choi et al. 2023) is a diffusion- based hierarchical VC system for voice conversion. For voice style transfer, Diff- Diff-HierVC uses two diffusion models, Diff and DiffPitch. We use BigVGAN vocoder and pre-trained model weights with default hyperparameters.
YOU (Casanova et al. 2022) was originally a text-to-speech model based on the end-to-end VITS but with a separate speaker embedding (from a speaker recognition model) to encode speaker identity. The inclusion of speaker encoding allows the use of YourTTS in a zero-shot voice conversion manner by simply substituting the embedding of one speaker with the embedding of another. Coqui implementation of the model is used for generation.
DifIGAN-TTS (Liu et al., 2022) is a Denoising Diffusion Probabilistic Model (DDPM) based TTS model. It is based on denoising diffusion generative adversarial networks (GANs), which adopt an adversarially-trainee expressive model to approximate the denoising distribution. The shallow model trained on the VCTK dataset for 400000 steps is used. We assign unique speaker ID from VCTK data to an identity of a sub-group and create spoofs within train or test of sub-groups.
GradTTS (Popov et al. 2021a) is a text-to-speedt model with score- based decoder producing mel-spectso by gradually transforming noise predicted by the encoder and aligning with text input employing Monotonic Alignment Search. GradTTS uses HifiGAN
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vocoder. We use the pre-trained weights of the model and vocoder trained on the LibriTTS dataset. A unique speaker ID from VCTK data is assigned to an identity in a sub-group. and spoof' are created within train test set of sub-gron sub-groups.
Audio- Video Synchronization: We combine the audio and video using the Audalign (Alignment). It is an open-source project that uses audio fingerprinting, coosscorreeation, cross-correlation with spectrograms, or visual alignment techniques to align audio and video timestamps. The extracted audio from the real video is combined with face-swapped manipulated videos, giving ii) Real Audio and Fake Video class. Moreover, we also combined the generated audio spoofs after syncing with real and face-swapped video. This gives us the two other classes of dataset: iii) Fake Audio and Real Video and iv) Fake Audio and Fake Video. This gives us all four classes of the generated dataset.
dataset's
B.2 SET B: AI GENERATED CONTENT
With the advent of generative Al methods, the amount of completely synthetic media across modalities has surged in social media platforms. These methods better capture contextual information since they generate media based on a textual prompt or guidance from pre-existing media. In this set, we present subset of completely synthetic AI Al-generated content. The generation procedure for each modality is discussed below in detail-
Video Generation: We use the MSRVTT (Xu al.) 2016) dataset for video-caption pairs. It is a large-scale dataset for open-domain video captioning containing video clips annotated with 20 English sentences by Amazon Mechanical Turks. From the publicly available dataset, 7010 captions are used to generate videos. For this, we use three different text-to-video models: Tex12Video- (Khachatryan et al., 2023), ModelScope (Wang et al. 2023a) and ZeroScope (Zrrosoppe). 24 frames are generated for every video from Exxt2idee-Zerr and ZeroScope, and 40 Trames are generated for each from ModelScopeT22 T2V. More frames are generated from ModelScopeT22 because the videos generated from this model have Shutterstock watermark. The higher number of frames can separately be used later to check how different detection algorithms perform on samples that contain watermark. Watermarks have become increasingly common in internet content more organizations prefer to add their watermark on their respective media. Each of the generative video models discussed briefly below:
ModelScope (Wang et al. 2023a) is a generative text-to-video synthesis model that evolves from Stable Diffusion. It uses spatiotemporal blocks, VQGAN, text encoder, and denoising UNet, resulting in consistent frame generation and smooth movement transitions. The videos generated from this model have a Shutterstock watermark. We use its public implementation, which is available in the Huggingface diffusers library.
Zeroscope (Zeroscope) is generative model based on ModelScope itself, but it has been improved to produce higher-qualit videos with a fixed aspect ratio and no Shutterstock watermark. We used zeroscape-v2-5766
Tex (Khachatryan et al., (023) a zero-shot text-to-video generation model. It uses existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain. Mainly, includes frame-level self-attention using new cross-frame attention of each frame on the first frame preserve the context, appearance, and identity of the foreground object.
We use the MusicCaps (Agostinelli et al. 2023) dataset to generate music samples, a large-scale dataset containing 5,211 music examples, each labelled with an English aspect list and a free text caption written by musicians. For each caption, namely: AudioLDM (Liu et al., 2023). MusicGen (Copet et al. 2023) and MAGNeT (Ziv et al., 2024). Further, download the original audio clip from Youtube, and corresponding each audio clip, we use an audio to-iioooo-udooo model: Audio Diffusion (Face). Each of the generative models are discussed below:
Audio Generation:
three text-to-music models,
MusicGen (Copet et al., 2023) is an end-to-end model for text-to-audio generation specif- ically tailored for music generation. It comprises single-stage transformer language
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model capable of generating conditioned high-quality music samples. We use its public implementation from the AudioCraft library.
MAGNeT (Ziv et al. 2024) is an end-to-end model for text-conditioned music generation.
It is Masked Audio Generation method utilizing single aon-autoreeressivv transformer. Its public implementation from the AudioCraft library is used.
AudioLDM (Liu et al., 2023) is an end-to-end model for text conditioned audio generation. A latent diffusion model (LDM) learns continuous audio representations from CLAP latent. We used its public implementation from the diffuser library.
Audio Diffusion (Face) is an end-to-end model for audio to audio generation. It converts audio input into mel spectrogram. then uses a Variational Auto-Encoder (VAE) model to encode and decode images to and from latent, and finally a UNeeDCCondttioMoode to denoise the encoded image latents. Public implementation from the diffusers library is used for generation.
Image Generation: We use the training set of the COCO dataset to get 118,287 image-caption pairs. Now, corresponding to each caption in the dataset, we use four text-to-image models, namely: Stable Diffusion XL (Podell et al., 2023), Kandinsky 2.1 (Razzhigaev et al. [003)), MultiDiffusion (Bar- Tal jet al. )023) and SDXL-Turee (Sauer et al. 2023). Further, the original image from the dataset is kept as the corresponding real for each generated image. We discuss each of the generative models below:
Stable Difffusion XL (Podell et al. 2023) an end-to-end model for text-conditioned image generation and is an improvement to the previous stable diffusion models with much larger UNet and a combination of OpenCLIP ViT-bigG/14 text encoder with the original text encoder. We used its public implementation from the diffusers library.
Kandinsky 2.1 (Razzhigaev et al., 2023) is an end-to-end model for text-conditioned image generation. As the text and image encoder, it uses the CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities to generate photo-realistic images. We use its public implementation from the diffusers library.
MultiDiffusion (Bar-Tal Tal et al. 2023) is a recent technique for text-conditioned image generation, making any pre-trained text-to-image model usable for generation without fine-tuning and training with a higher control over output. We used it in conjugation with aaabeeDifussionPaaaaaaaiiii diffusers library.
available in its public implementation from the
SDXL-Turbo (Sauer et al., 2023) is an end-to-end model for text to image generation. is distilled version SDXL-1.0 and uses a novel training method called Adversarial Diffusion Distillation (ADD) for much faster, high-quality generation. We used its public implementation from the diffusers library.
Synthetic-Faces Dataset: We scrape image samples from a popular deep fake generation website thi spe wpwrspondeeee www.ddseenneeeeeeoooeooeeeeeeeee st comm Further, we classified these samples per and skin tone into eight subgroups to create a demographically balanced dataset that contains 1025 synthetic faces for each subgroup cumulating to total of 8,200 synthetic face images. Now, to have an equal number of real faces in our dataset, we collect an equal number of samples from Balanced Faces in the Wild (BFW) (Robinson) 2022).
B.3 SET C: FAKES IN THE WILD
We use Midjourney (Midjoumey) and Artguru (ArtGuru), both premium tools that allow users to generate synthetic images via text-to-image modality through a premium API and a web interface, respectively. For Midjourney, we use prompts from the validation set of the COCO dataset, and for each prompt, we get four corresponding images to generate a total of 20,000 images. Artguru, however, specializes more in generating identity-dmi driven images for given prompt. Therefore. randomly pick 1000 prompts from the person class of the COCO dataset to generate a total of 1000 images. Apart from these images, have 24 real-world doctored videos used for testing.
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Published as a conference paper at ICLR 2025
C BASELINE MODELS
In our evaluation of the ILLUSION dataset, we employ a diverse set of eleven deepfake detection methods. The selection of these methods is primarily guided by the availability of their respective codebases. For each method, we adhere to the default parameters provided, ensuring a fair and consistent comparison across all detection methods. In the following sections, provide succinct discussion of each of these detection methods:
C.I
VISUAL. MODELS
MesolnceptionNet (Afchar et al. 2018) a CNN architecture used for deepfake and face- to-face image forgery detection. It develops on the Inception module. Going deeper with convolutions for the first two convolutional layers of the Meso-4 architecture, Mesolnception- created.
XceptionNet (Chollet, 2017) a deep CNN architecture using depthwise separable convo- lutions. Inception modules in CNNs can be seen transitional stage between conventional convolutions and depthwise separable convolutions, which combine depthwise and point- wise convolutions. This insight leads
Inception-inspee deep CNN design, where depthwise separable convolutions replace Inception modules.
DSP-FWA (Li & Lyu, 2018) It uses warp-wg artifacts from deepfake generation to identify swapped videos. It employs CNN trained with negative examples created by mimicking affine face warping steps and positive face images. To simulate artifacts, these negative examples involve recognizing faces, resizing them, applying Gaussian blur, and then affine warping. The technique uses CNN models like VGG16 and ResNet to detect inconsistencies between manipulated facial regions and their surroundings.
F3Net (Wei et al. 2020) or Frequency in Face Forgery Network is network that deep mines the forgery patterns through two-stream collaborative learning framework, utilising two complementary but distinct frequency-awart clues: 1) frequency-awan decomposed image components, and local frequency statistics. It uses DCT frequency-doman transformation.
C.2 AUDIO MODELS
RawGAT-ST (Tak et al. 2021). is ppectro-temporn graph attention network that learns to detect spoofed speech from raw waveform inputs. It creates separate spectral and temporal sub-graphs, employs graph attention capture relationships within each sub-graph, fuses the sub-graphs at the model level, and uses graph pooling to improve discrimination. This approach automatically learns relevant spectro-temporal artifacts without hand-crafted features or separate systems for different attacks.
AASIST (uun et al., 2022)
Seention-Augmenter Speaker Inconsistency Stacking Transformer for detecting spoofed utterances. It uses a heterogeneous stacking graph attention layer that models artifacts spanning both temporal and spectral domains using heterogeneous attention mechanism and a stack node. The model employs a max graph operation with competitive mechanism and an extended readout scheme.
SSLModel (Tak et al., 2022 proposes embedding fusion scheme that combines represen-
tations from supervised learning (SL) and self-supervised learning (SSL) models for spoofed speech detection. It performs weighted score fusion between SL and best SSL models and then minimizes the embedding distribution between selected SL and SSL representations using comprehensive statistical analysis to select appropriate layers. This fusion approach leverages knowledge from both SL. and SSL frameworks to improve performance.
Conformer (Gulati et al. 2020) model has statistical pooling for jointly performing speaker verification and voice spoofing detection. It utilizes the Conformer architecture, which combines Transformer and CNNs and employs statistical pooling aggregate frame-level representations. The model is trained on both tasks simultancously, enabling it to reject both spoofed utterances and utterances from different speakers.
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Published as a conference paper at ICLR 2025
C.3 MULTI-MODAL MODELS
FACTOR (Reiss et al. 2023) is a detection method that utilizes fact-checking adapted from fake news detection. It verifies the claimed facts (c.g., identity) by comparing them with the actual observed media (c.g., appearance) to identify real versus fake content. does not require training, uses readily available features, and achieves higher accuracy without prior exposure to deepfakes, especially in face-swapping and audio-visual synthesis scenarios.
MRDF (Zou et al., 2024) proposes cross-modality and within-modality regularization to maintain modality distinctions during multi-modal representation learning. The approach includes an audio- vusial transformer module for modality correspondence and a cross- modality regularization module align paired audio-visual signals. Additionally, within- modality regularization module refines unimodal representations to retain modality-specific details.
Unimodal Ensembling integrates ensemble of two best performing models, F3Net and SSLModel. For any given deepfake input, we first extract the video and audio components. Subsequently, we fuse the predictions derived from each modality to arrive at final pre- diction. This process ensures a comprehensive analysis of the deepfake content, leveraging both visual and auditory cues.
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Reviews:
[{"review_id": "QzVil20vvs", "reviewer": "Reviewer_VKJA", "summary": "Glad to review the work.\n\nThis work proposes a large-scale multi-modal deepfake dataset that encompasses samples from multiple aspects. \n\nIn general, I consider the constructed dataset could be helpful to related researchers.", "questions": "My main concerns are listed in the weakness part, where the weaknesses (1) and (2) are expected to be responded.\n\nI will consider changing my score based on responses and other reviews.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "In addition to constructing a large dataset, the authors evaluate many deepfake detectors from five aspects, summarize their findings, and discuss potential implications, the design of the work is complete.\n\nThe constructed dataset is comprehensive, covering different modalities, languages, and generation approaches.", "weaknesses": "Some of my concerns are listed as follows.\n\n(1) Regarding the dataset, although it is generated using different aspects of algorithms, I doubt whether the samples in each set are with certain relationship (rather than randomly combining data), e.g., the voices generated using different algorithms are from the same source \"voice\" or \"text\", e.g.,  the pipeline in Set A are from the same \"source\" in the beginning of Figure 3 (similar in other sets).\nIn this case, it will be useful to evaluate and compare the performance of deepfake detectors.\n\n(2) Regarding baseline detectors, although it mentions they are state-of-the-art, more categories or reviews on them are necessary to support why they are selected, whether they are type-complete or typical. The selection of baseline detectors may result in different findings or implications.\n\n(3) Finally, I believe the work could provide potential reference to related researchers, however, I doubt whether the contribution of this work is enough for ICLR.\nAlthough this work proposes a large dataset and conducts multiple evaluations, I am afraid it lacks some technical contributions, e.g., new generation approaches, or the dataset could help to improve deepfake detectors. \nOn the other hand, the empirical evaluations could also be explored through other datasets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eTKfjBL7uY", "reviewer": "Reviewer_79Vw", "summary": "This paper adopts various forgery generation algorithms to create a large-scale dataset containing four subsets, and raises five questions from three aspects: multimodal identity exchange, detection system generalization and detection of forgery or generation methods, and conducts related experiments.", "questions": "In the data set of video generation, image generation, audio generation did not separate the clear description, the combination of a very poor look, video 100,000 magnitude, image million magnitude, in the scale is not small, only the million magnitude is not rigorous, there is still room for improvement in the logic of the discussion. In addition, the problems discussed in this paper can also be realized by using existing data sets, so I think the innovation of this paper is not enough, although this data set has certain value.\nIt is suggested that the results of the experiment should be further explored and analyzed, and it would be better to put forward some innovative solutions.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The authors propose a large-scale forgery dataset, which includes a variety of face-changing forgery, generation methods and speech forgery methods, and includes languages of many countries, which has certain originality.", "weaknesses": "The problems studied in this paper are the focus of current research, not the author's new definition. The object of this paper is to put forward a large-scale and organized data set to explore the problem and support relevant research, which I think has certain value. However, it seems that the existing data sets can also be used to explore the five questions raised, and the author only gave the relevant experiments on four questions, the exploration and analysis of the experimental results are not deep enough, and the proposal of innovative solutions is not innovative enough for a top conference, and there is still room for further improvement in the writing logic of this paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "C3GyWoX4mn", "reviewer": "Reviewer_eYMp", "summary": "This paper introduces ILLUSION, a comprehensive multi-modal and multi-lingual deepfake dataset containing over 1.3 million samples. The dataset uniquely combines identity manipulations, synthetic content across multiple modalities (image, audio, video), and real-world test samples spanning 26 languages, generated using 28 different generation techniques. Through extensive benchmarking experiments with state-of-the-art detection methods, the authors demonstrate critical challenges in deepfake detection, including poor generalization to unseen generation methods, performance degradation in multi-lingual contexts, and limited effectiveness on real-world samples. The work provides valuable insights into the current limitations of deepfake detection systems and establishes a foundation for developing more robust detection approaches.\nThe main contributions include: (1) the first large-scale dataset combining multi-modal and multi-lingual deepfakes, (2) balanced representation across demographic attributes, and (3) comprehensive evaluation across various challenging scenarios revealing current detection limitations.", "questions": "1. What is the purpose of including \"This Person Does Not Exist\" samples in Set B? The relationship between these synthetic faces and the natural images in Set B needs clarification.\n\n2. The authors should explain the unusually high accuracy of MesoInceptionNet on Set C while other methods perform poorly (Table 4). This significant performance disparity requires investigation and explanation.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The paper presents a new dataset with impressive scale, content diversity, and multi-modal richness, demonstrating substantial effort in data collection and curation.\n\n2. The incorporation of multi-lingual samples fills a significant research gap in the field of deepfake detection.\n\n3. The authors propose a diverse evaluation strategy with four different protocols, including the consideration of real-world applications like Midjourney, which enhances the practical value of their work.", "weaknesses": "1. The performance evaluation is somewhat limited with only four detection methods. While this isn't the paper's main contribution, including more methods would provide a more comprehensive assessment of the dataset's quality. The authors should consider adding classical general face forgery detection methods (like LTW[1], DCL[2], SBI[3], SLADD[4] e.t.c.), AIGC detection methods for Set B (like NPR[5]), and standard baselines (EfficientNet-b4[6] and VIT series).\n\n[1]. Domain general face forgery detection by learning to weight\n\n[2]. Dual contrastive learning for general face forgery detection\n\n[3]. Detecting Deepfakes with Self-Blended Images\n\n[4].Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection\n\n[5].Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection\n\n[6]. Rethinking Model Scaling for Convolutional Neural Networks\n\n2. The authors should provide a deeper analysis of Table 4's results, particularly explaining the substantial performance variations between different methods on Set B and Set C.\n\n3. The audio forgery aspect presents an interesting dimension that deserves more thorough analysis, specifically examining how different types of audio manipulation affect overall deepfake detection performance.\n\n4. The robustness evaluation could be more comprehensive. While c23 and c40 compressions are considered, additional real-world noise simulations like JPEG compression and resolution variations would better assess the dataset's practical utility.\n\n5. The quality assessment is primarily statistical, lacking detailed analysis through visualizations of different methods' outputs or quality evaluations for each category.\n\nDespite the above issues, I believe this paper makes a significant contribution to the field. I recommend accepting this paper, contingent upon the authors making their complete dataset publicly available and ensuring that the released dataset fully matches the specifications described in the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PAH89wwG0G", "reviewer": "Reviewer_7EH8", "summary": "This manuscript presents ILLUSION, which is a large-scale multi-modal deepfake dataset. The author(s) believe(s) the comprehensive nature of the proposed dataset enables researchers to develop and evaluate more resilient deepfake detection methods, addressing the evolving landscape of synthetic media threats.\n\nAs the current popularity of generative artificial intelligence, it is inevitable to propose a complete data set, especially considering multimodal data. However, for the forgery identification algorithm, whether the generalization ability is achieved is an important indicator to solve today's identity forgery. Many forgery identification algorithms are based on deep learning, which generalizes the concept of differentiation from real images by extracting depth or potential features. This has to mention the importance of data sets. Based on my experience, the following advantages, disadvantages and questions I hope the author can reply to are summarized for this manuscript.", "questions": "- The author(s) take(s) into account the multi generative model to enrich the diversity of forged data. Generative models often have digital fingerprints [1]. How do(es) the author(s) consider the differences between the data set proposed in this paper and the existing data set at the level of digital fingerprints?\n\n- According to Q1, the author is requested to provide the degree of overlap between the digital fingerprints of the data set proposed in this paper and the existing data set, and to elaborate the advantages and disadvantages.\n\n- In order to verify the validity of the proposed dataset, the authors used the detection model such as SSLModel for verification (Table 7). As we all know, deep learning models have data specificity. Whether these models have some improvement in generalization. If not, this part of the experiment is not very convincing.\n\n- The data set formed in this paper is based on the training of generated samples. However, whether these fake samples can represent forgery in the real world. I mean, when the detection model trained based on this dataset encounters real-world samples, does it recognize forged features or synthetic data?\n\n**Refs:**\n\n    [1] Yu, N., Skripniuk, V., Abdelnabi, S., & Fritz, M. (2021). Artificial fingerprinting for generative models: Rooting deepfake attribution in training data. In Proceedings of the IEEE/CVF International conference on computer vision (pp. 14448-14457).", "limitations": "", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The author aims at the current hot issues of generative AI, and the contribution of this manuscript is expected to be called solving the ethical problems of generative AI.\n\n- This paper collates the information of the current dataset and embodies the advantages of ILLUSION.\n\n- Many datasets tend to pursue a huge number. The datasets proposed in this paper enrich the current research from the perspective of multimodality.", "weaknesses": "**Some major comments:**\n\n- As other methods of generalized image forgery, such as digital watermarking, PS, image optimization, etc. Whether the author considers to include it in the falsification data set, or how the author thinks about the falsification hazards brought by this kind of data.\n\n- The author provides a large-scale data set, which is essential in today's research direction focusing on large models. However, does the author consider that the larger the data size, the better? Some studies have shown that data validity (such as reducing samples with similar features, reducing samples with overlapping features, etc.) often takes precedence over data size. The author also mentioned the disadvantages brought by the large data scale in the BROADER impact. The author is requested to analyze from the perspective of data validity whether the gain brought by this dataset on the large model can make up for the defects brought by invalid data.\n\n**Some other minor comments:**\n\n- The font of Figure 1 and Figure 2 is too small, please adjust according to the requirements.\n\n- The author used a large number of face images in the dataset. Are these images exempted?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Jb1quDcl9D", "reviewer": "Reviewer_pXKi", "summary": "In this paper, the authors introduce a large-scale, comprehensive deepfake dataset containing nearly 30 different forgery methods, divided into four subsets. The dataset includes multi-modal and multi-lingual fake videos, with a balanced distribution across gender and age groups. The authors also conducted exploratory benchmarking on their dataset and highlighted the challenges that existing detectors face when identifying deepfakes in real-world scenarios.", "questions": "Please see Weaknesses", "limitations": "", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- A large-scale dataset containing various forgery methods is proposed.\n\n- A benchmark for the proposed dataset is constructed.", "weaknesses": "- Uniqueness of the dataset. As a large-scale multimodal dataset, ILLUSION does not exhibit particularly distinctive features. Previous datasets, such as ForgeryNet [A] and KoDF [B], have already focused on large-scale video and image forgeries. Additionally, many recent datasets have been proposed for the detection of AIGC content [C][D][E][F][G]. While the integration of various forgery methods is certainly commendable and meaningful, further discussion is needed on how this integration can contribute to advancing research.\n\n- The comparison with related datasets is insufficient. The authors should enhance Figure 1 by including, but not limited to, datasets in [A]-[G].\n\n- The authors are advised to include more state-of-the-art deepfake detectors to create a more comprehensive benchmark.\n\n[A] ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis\n[B] KoDF: A Large-scale Korean DeepFake Detection Dataset\n[C] DIRE for Diffusion-Generated Image Detection\n[D] Diffusion Facial Forgery Detection\n[E] DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis\n[F] GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image\n[G] On The Detection of Synthetic Images Generated by Diffusion Models", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)