Tell me Habibi, is it Real or Fake?
Authors: Kartik Kuckreja
Parul Gupta
Injy Hamed
Thamar Solorio
Muhammad Haris Khan
Abhinav Dhall
Venue: ICLR 2026 Poster (2026)
Keywords: Deepfakes
multilingual
multimodal
code-switching
Primary area: datasets and benchmarks
TL;DR: we introduce ArEnAV, the first large-scale Arabic-English audio-visual deepfake dataset featuring intra-utterance code-switching, dialectal variation, and monolingual Arabic content.

Abstract: Deepfake generation methods are evolving fast, making fake media harder to detect and raising serious societal concerns. Most deepfake detection and dataset creation research focuses on monolingual content, often overlooking the challenges of multilingual and code-switched speech, where multiple languages are mixed within the same discourse. Code-switching, especially between Arabic and English, is common in the Arab world and is widely used in digital communication. This linguistic mixing poses extra challenges for deepfake detection, as it can confuse models trained mostly on monolingual data. To address this, we introduce ArEnAV, the first large-scale Arabic-English audio-visual deepfake dataset featuring intra-utterance code-switching, dialectal variation, and monolingual Arabic content. It contains 387k videos and over 765 hours of real and fake videos. Our dataset is generated using a novel pipeline integrating four Text-To-Speech and two lip-sync models, enabling comprehensive analysis of multilingual multimodal deepfake detection. We benchmark our dataset against existing monolingual and multilingual datasets, state-of-the-art deepfake detection models, and a human evaluation, highlighting its potential to advance deepfake research. The dataset is [public](https://huggingface.co/datasets/kartik060702/ArEnAV-Full).

Published as a conference paper at ICLR 2026
TELL ME HABIBI, IS IT REAL OR FAKE?
Kartik Kuckreja," > Parul Gupta, Injy Hamed, A Thamar Solorio, Muhammad Haris Khan, Abhinav Dhall MBZUAI <Monash University
ABSTRACT
Deepfake generation methods are evolving fast, making fake media harder detect and raising serious societal concerns. Most deepfake detection and dataset creation research focuses monolingual content, often overlooking the challenges of multilingual and code-switched speech, where multiple languages are mixed within the same discourse. Code-switching, especially between Arabic and English, is common in the Arab world and is widely used in digital communication. This linguistic mixing poses extra challenges for deepfake detection, models trained mostly on monolingual data. To address this, we introduce ArEnAV, the first large-scale Arabie-English audio-visual deepfake dataset featuring intra- utterance code-switching, dialectal variation, and monolingual Arabic content. It contains 387k videos and over 765 hours of real and fake videos. Our dataset is generated using novel pipeline integrating four Text-To-Speech and two lip-sync models, enabling comprehensive analysis multilingual multimodal deepfake detection. We benchmark our dataset against existing monolingual and multilinguul datasets, SOTA deepfake detection models, and a human evaluation, highlighting its potential to advance deepfake research. The dataset is public
can confuse
INTRODUCTION
Deepfake technologies, involving the artificial generation and manipulation of audio-visual content, have rapidly advanced, significantly complicating the task of distinguishing real media from synthetic creations. The potential misuse of deepfakes for misinformation, defamation, or impersonation presents profound societal risks, driving substantial research into their detection. Although initial deepfake research primarily focused on manipulating individual modalities, audio-only (Todisco et al.) 2019) or video-only (Jiang et al., 22200; Kwon et al., 2021 Li et al. 2020b), recent developments increasingly consider joint manipulation audio and visual streams for more realistic synthesis.
A significant gap remains in existing deepfake datasets (Table which largely overlook multilingual scenarios, particularly code-switen pode-switching (CSW), despite its global prevalence among bilingual speakers. In the Arab world, CSW is prominent feature of daily communication, serving not only linguistic tool but also as a marker of cultural identity and social context (Hamed et al., 2025). Arabic speakers frequently alternate between Arabic and English within the same sentence, such as:
Wu e*ع deepfake detection J) موفيع (The topic of deepfake detection is very important).
This challenge compounded by the diglossic nature of Arabic (Ferguson 1959), comprising of two main varieties: Modern Standard Arabic (MSA) and Dialectal Arabic (DA). MSA functions as a lingua franca across the Arab world and is primarily used in formal settings. DA, belonging to each country, is used in everyday conversations and informal writing. Given that Arabic is one of the most widely spoken languages worldwide, ranked fifth by number of Standard Arabic speakers (Ethnologuel 2025), handling its diglossic variation and code-switching phenomena is essential for building deepfake detection systems that address the linguistic diversity in real-world media content.
Recent studies provide compelling evidence of how common CSW is among Arabic speakers. The ZAEBUC-Spoken corpus (Hamed et al. 2024) reveals that approximately 19% of spoken utterances
kuckre a, injy hamed, thamar solorio, muharmad, haris ss) @mbzual. ac. (parul, abhinav nav. dhal1 aoroonssh.ed...

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Published as a conference paper at ICLR 2026
a) Data Generation Pipeline
b) #Hours
Piado S
Sprug فلليي not bad."
"لن ("The wosther is bad!")
Tes-155
169 PNL
14M4 الولل (*The wonk"
Juin المول ("Thh boya sat.))
Shihhop ("Wie create hope!)
("We create
Sync-Nei
Yolo-v5-Face
Whisper
Ciip
c) #Unique Videos
Annery
Repiore
Dolete
Fideo Manippimion
1.1-1.866 (2..0)
Jinins 117
8,800 videos
48,000 clips
Optivn I: Deflipp
Opaice
CSW
200
Latest-Spee
Transcript Maripulation
Ruf Frames
(1.88
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#Clips
"aparty "aagaap anr Teeange Teadet ont "odoos" yor "oaft "Lokel....
1000
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(59.9%)
"pprndion" //apanation Thad wour Moef enc weat Ast "vsse" A.ft
Option J.
Option
Fiece Chastyy
(9.9%)
"spoudio" (y"erraan"" "oneleee Oeiginal shalee? 2ns
Figure 1: a) We show the data generation pipeline for ArEnAV dataset. In a) input videos are analysed for audio, face, and text extraction. Using few-shot prompts with GPT-4. 1 1-mini, CSW-based spoken text manipulation is performed. This is followed by speech and face enactment generation. b-d) The plots show the data splits and CSW distribution. Here is example of CSW input and manipulated text with translations in parentheses: تصننع hope ("We create hope.") تصننع fun ("W create fun.")
exhibit CSW, having an average of 44% English words. The corpus also highlights the presence of diglossic CSW between Arabic variants. Similarly, the ArzEn corpus (Hamed et al. 2020 demonstrates high CSW frequency, where 63% of utterances involve CSW with approximately 19% of words being English. These findings highlight the extent to which Arabic-English CSW is not merely an incidental phenomenon, but widespread communicative strategy. Despite its ubiquity, deepfake detection systems remain largely ill-equipped to handle such language alternation, focusing predominantly monolingual data. Addressing this important oversight, work seeks to bridge this critical gap by introducing the first Arabic-Englnn English CSW audio-visual deepfake dataset, thus advancing the field toward more relevant detection systems. Our core contributions are follows:
Introduction of ArEnAV, the first large-scale Arabic-English audio-visual deepfake dataset featuring intra-utterance code-switching and dialectal variation, including both bilingual and diglossic switching across Modern Standard Arabic, Egyptian, Levantine, and Gulf dialects, addressing critical gap in multilingual deepfake research.
A novel data generation pipeline tailored to English and Arabic (MSA and dialect-rich content), integrating four TTS (Text Speech) and lip-syne models.
A comprehensive amalysis contrasting our dataset against existing monolingual and multilin- gual datasets, existing state-of-the-arr (SOTA) deepfake detection models, and detailed User Study, underscoring its unique difficulty in detection by models and humans alike.
2 RELATED WORK
Early deepfake research was predominantly monolingual and modality-specific. Initial significant contributions included vidco manipulation techniques such FaceSwap and Face2Face as introduced by Thies et al. (2020), which led to seminal datasets like FaceForensics** (Rössler et al. 2019) and the DeepFake Detection Challenge (DFDC) (Dolhansky et al. 2020a). These datasets primarily provided facial manipulations within
contexts, focusing largely on visual realism.
Parallel to video deepfake advancements, audio deepfakes evolved rapidly, driven by progress in -sxeet (TTS) synthesis, voice conversion, and generative audio models such as Tacotron (Wang et al. 2017). Datasets like ASVspoof (Wang al. 2020) and WaveFake (Frank & Schönherr
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Published as a conference paper at ICLR 2026
2021) contributed significantly by providing benchmarks to evaluate audio manipulation detection methods, albeit still largely restricted to English.
In recent years, research has expanded towards joint audio-visual deepfake manipulations. Datasets such as FakeAVCelcb (Khalid et al., 2022) showcased realistic lip-synced speech synthesis in tandem with facial manipulations. AV-Deepfake IM (Cai et al. 2024a) further advanced this domain by automating transcript alterations to create nuanced, localized audio-visual deepfakes, highlighting the necessity of detecting temporally and contextually subtle manipulations.
Recently, there has been increased focus on multilingual audio deepfakes. These efforts have revealed key limitations in generalizing detection models across languages and proposed new resources to address these challenges, Marek et al. (2024) conducted a comprehensive study on cross-lingume deepfake detection, showing that models trained on one language often fail to generalize effectively to others, underscoring the role of Einguage-specifi phonetic and prosodic features in model per- formance. Multilingual audio-visual datasets emerged even more recently to address the global dimension of deepfake threats. The PolyGlotFake dataset (Hou et al.) 2024) contains audio- visual deepfakes across seven languages. Although the dataset covers a wide range of language, the size of the real data is significantly small. Nonetheless, these multilingual datasets remain limited to either monolingual or monomodal scenarios within each single instance, igmoring the prevalent reality of intra-utterance language switching.
Our work directly responds to this critical gap. Unlike previous studies, we not only incorporate multilingual content but also explicitly generate intra- utteranceeecce code-switched audio-visual deepfakes. We leverage SOTA TTS and lip sync methodologies adapted for multilingual use, resulting in realistic, diverse, and challenging benchmarks.
i) #Train
ii) #Val
iii #Test
Tage Falrrag 5%
LarerSee VTTS
Merkig
g00%
Messigr Daaker
Mesing-
Hossng
(350
0.35
Figure 2: Dataset distribution for i) Train, ii) Val and iii) Test split. The outer layer shows the split between various combinations of Text-to-Speech and Lip-Sync models used for audio-visum manipulation. The middle layer shows the distribution of language in the original transcript, which is either Ar (Arabic) or CSW (Code-Switched English-Aran Arabic.. The inner layer shows the distribution of different operations applied to the original transcripts, "meaning only", "dialect+meaning"" and "meaning + translation" (For fine- -raiggd detail about what they entail, refer to Table [6]in Appendix.)
Table 1: Details for publicly available deep-fake datasets in chronologically ascending order. Cla: Binary classification, SL: Spatial localization, TL. Temporal localization, FS: Face swapping,
RE: Face reenactment, TTS: Texe-to-speech, VC: Voice conversion.
Datasel
Year
Tanks
Munip. Modality
Method
#Total
Multilingual
Code Switching
Google
Nick Andrew 2019
V
FS
3,431
DFDC
er
Deeper Rarennee Dimne mi 2000
FS
60.000
De Liet dr 201ann
2020
Cla
FS
6,220
X
KeDF
KKbi [ 7024
2021
Cla
V
FS/RE
237,942
X
FakeAVezion knn
2022
2021
Cla
AV
REFES
25,5001
ForgeryNet [Iter
2021
SL/TL/Cla
V
Random PSRE
221.247
ASVSpuul M0rr (RTEE
TTS/VC
593,253
Car
driven FS
136,304
DE Planer (olaarire ol
2023
Cla
265
AV-IM Careral 21rag
2023
TL/Cla
AV
Content-driven RE/TTS
1,166.760
X
PolyGleDDkz
AV
RE/TTS/VC
15,238
Illusion (Tbakhi
2025
AV
PS/RE/TTS
1,316.77
2025
Cl/T11
AV
Content Driven RE/TTS/N
387,672

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Published as a conference paper at ICLR 2026
3 ARENAV DATASET
ArEnAV is a large-scale audio-visual deepfake dataset specifically focused on Arabic-English CSW. Comprising approximately 765 hours of video data sourced from 8,809 unique YouTube videos, ArEnAV establishes itself as the first and most extensive benchmark for multilingual deepfake detection (see Table for dataset comparison). The dataset is constructed to preserve the original identity and environmental context of the source videos while systematically manipulating the semantic content to introduce Arabic-Englise CSW. Following the taxonomy proposed by Cai et al.) (2024a), ArEnAV includes three manipulation strategies: Fake Audio & Fake Video: Both audio and visual content are synthetically generated, simulating complete audiovisual deepfakes. Fake Audio & Real Video: The audio track is manipulated to introduce anti-semantic and CSW content while maintaining the original visual content. Real Audio & Fake Video: The original audio retained, while facial movements and lip synchronization are altered to create visually deceptive content.
3.1 DATA COLLECTION
We use the YouTube video links from VisPer's Arabic Train subset (Narayan et al. 2024). We chose VisPer because it the largest publicly available non-English audio-visual corpus, with over 1,200 hours of Arabic alone. Its 200-keyword crawler ("interview,", "tutorial," etc.) pulls videos spanning talk shows, vlogs, documentaries, and lectures, mirroring the broad-coverage strategy that is required for fair and diverse representation of a culture-specifie deepfake dataset. We first run scene change detection model to split the video into clips, and then we use Yolo-v5 to obtain the faces in each frame well track them across frames. Since we did not have ground truth for transcripts for VisPER, we surveyed oattc--f-thh-att Automatic Speech Recognition (ASR) models for Arabic, based on the Arabic ASR Leaderboard HuggingFace. Following qualitative comparison, we finalized the Whisper-v2 (finetuned on English-Arabic data) for our method, with the default output language set to Arabic. Following the transcripts, apply Forced Alignment between the audio and text, using a multilingual wav2vec2 model (Baevski et al., 2020) supporting both Arabic and English. This provides us with word-level timestamps for code-switched Arabic and English data.
3.2 DATA GENERATION PIPELINE
The data generation pipeline roughly consists of three stages: transcript manipulation, audio gen- eration, and video generation. First, we apply controlled modifications to the transcript. Secondly, we synthesize new audio for the altered transcript while preserving the speaker's voice character- istics. Finally, we render a lip-synced video that matches the new audio, producing a realistically manipulated video clip. We detail each stage as follows.
3.2.1 TRANSCRIPT MANIPULATION
We leverage GPT-4. 1-mini OPenAl 2025) to perform content-driven modifications of our multilin- gual transcripts. We define eight distinct transcript change modes that span both code-switched and Arabic-only contexts, allowing fine- ane-grained control over how the transcript is altered. These modes include three main operations: first, meaning only, which only involves changing the meaning of the word and keeping the language it is, second, meaning dialect, which involves changing the meaning of the word and changing its language to another Arabic variant (either MSA or any dialect), and lastly, meaning + translation, which asks the model change the meaning of the word, and then translate it to English. Table summariies the eight modification modes with their intended effect.
Table 2: Transcript manipulation rules in ArEnAV for Arabic (AR) and English (EN) words.
#
Original Transcript
Original Word
Inserted Word
Operation
1
CSW
EN
EN
Change meaning only (keep English)
2
CSW
AR
AR
Change meaning only (keep Arabic variant)
3
CSW
AR
AR
Change meaning change Arabie variant
CSW
AR
AR/EN
multi-op: When 2-3 ops - edit EN and 1-2 AR words
5
Arabic
AR
AR
Change meaning only (keep Arabic variant) Change meaning change Arabie variant Change meaning change language to English multi-op: Apply all operations on Arabic words
Arabic
AR
AR
Arahic
AR
EN
8
Arabic
AR
AR/EN
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By categorizing edits in this way, ensure controlled and diverse set of manipulations ranging from subtle word substitutions to introducing or removing CSW instances. Due to the effectiveness of few-shot prompting, we prompt GPT--.1-mmin with 15 examples, explaining various kinds of transitions and possible changes. Examples of original and augmented transeripts achieved by these manipulation rules are shown in Appendix A..] We provide the prompt in Appendix [A.6 We report text manipulations distributions as follows: replacement (94.6%), insertion (5.1%) 5.1%) and deletion (0.3%). These distributions reflect GPT's manipulation choices, which were not manually enforced.
a) Entailment Evaluation Total Videos
b) Perplexity Evaluation
i) Dialect + meaning Near 20m3
1) Jais-3B
Perplexity
Number of 2000
Videos
200
Reath
AestFaae Jer
RoarPalz Pate Refresnce Tixt
1it) Meaning only
(v) Meaning + Translation
i1
Qwen-225--7
50 0.20
Perplexity
Number
Videos
RealFake
ReaiFale
RaklFake Refonner
Entailment Quality Meon
Entailment Quality Mean
151
Figure 3: a) Entailment distribution over i) All change modes, ii) Dialect meaning, iii) Meaning only, and iv) Meaning + translation. b) Perplexity Evaluation distribution among dataset splits, showing perplexity calculated using i) Jais-3B, an Arabic-Englist LLM, and ii) Qwen-2.5-7B. b) Perplexity calculated using i) Jais-3B ii) Qwen-2.5-7B. Reference in both shows the perplexity calculated on an Arabic-Englishe CSW text dataset (SDAIANCAI 2025).
Transeript Quality:
To quantify the impact of our LLM-based manipulations, we employ two complementary metrics: Bidirectional Entailment Quality Mean: the average of Real-FFake and Rake-+Real NLI entailment scores (1.0 = full semantic entailment; 0.0 = direct contradiction) and Perplexity: how well language model predicts transcript (lower = more fluent/naatural). Table shows the distribution of entailment quality means over different types of perturbations. In every subset, large fraction of samples lies below the 0.5 threshold, and many even the contradiction zone, demonstrating that our pipeline reliably injects semantic change regardless of language or dialect.
Figure 3p reports average perplexities on real versus fake transcripts under two open-source LLMs; Jais-3B (Sengupta al. 2023) and Qwen-2.5-7B (Qwen al. 20225,, across the data splits. The minimal difference in perplexity shows that our fake transcripts remain fluent and natural, despite major changes in meaning. This balance between altered content and surface-level fluency is essential for generating effective audio visuall deepfakes
3.2.2 AUDIO GENERATION
The next step involves generating a synthetic audio track that precisely follows the edited transcript while maintaining the voice characteristics of the original on-screen speaker. Initially, we segment the audio into clean speech and background noise using Denoiser (Defossez et al., 2020). Conventional zero-shot voice cloning systems. such as YOuTTTS (Casanova et al. 2023), exhibit strong performance in English but struggle with Arabic phonetics and cross-lingual synthesis. To address this, we employ four targeted cloning strategies: a) XTTS-v2 (Casanova et al. 2024): A multilingual, zero-shot TTS model natively supporting Arabic, English, and code-switching b) XTTS-v2 (Casanova et al. 2024) + Voice-2 (Qin et al., 2024): When reference voice sample is available, we achieve higher fidelity by generating the utterance with XTTS-v2 and performing speaker conversion via Open Voice-v2. c) Fairseq Arabic TTS (Ott et al., 2019) + Open Voice-v2 (Qin et al.) 2024):
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For fully Arabic sentences, we generate audio with the Fairseq Arabic TTS, followed by speaker conversion using Open Voice-v2. d) GPT-TTS (OpenAI 2023) + Open Voice-v2 (Qin et al., 2024):
We randomly select one voice out of 29, generate the sentence, and then convert the audio to the target speaker's voice with OpenVoice-v?
The audio-generation flow depends on the edit type. For insert or replace operations, regenerate the complete sentence and validate the generated audio using Whisper-Turbo (Radford et al. 2022). retaining only samples that exactly match the intended transcript. This step ensures intelligibility and accurate timestamp alignment for splicing the segment into the original audio. If validation fails, we discard the sample. For delete operation, we remove speech segments entirely, preserving only background noise. Finally, after each edit, we normalise the loudness of the manipulated segment relative to the original audio and recombine with the extracted environment noise.
Audio Quality:
Table 3 presents the comparison of audio quality for ArEnAV based on speaker similarity, signal quality and distribution realism with existing audio-visual deepfake datasets. We report speaker encoder co- sine similarity (SECS), Signal-to-Noiss (SNR) and Fréchet audio distance (FAD) for recent Audio Visual datasets. SECS measures the speaker's voice similar- ity between generated clip and the real reference (range [-1, 1]. higher better), while FAD evaluates the distributional distance between the generated audio and real audio (lower is better). The metrics combined indicate that ArEnAV has high-quality audio samples.
Table Audio quality comparison across different datasets.
Dotaset
Langunge
SECS1
SNR(dB) FADU
FakeAVCeleh
English
0.543
2.16
6.598
LAV-DF AV-DeepfakeIM
English
0.984
7.83
0.306
English
0.991
9.39
0.088
ArEnAV
Arabic, English
0.990
7.65
0.140
3.3
VISUAL MANIPULATION
For video generation, after extensive experimentation, we chose two diffusion based lip-sync ap- proaches: Diff2Lip (Mukhopadhyay al. 2023) and LatentSyne (Li et al. 2025). Both of these models perform high-quality zero-shot lip-sync and are open-sourced. Using the newly generated audio and the original video's frames we generate the fake frames. For replace and insert word operations, we generate the fake frames for the new word, and for delete word operations, we generate a face with closed lips i.e., without audio.
Visual Quality: To evaluate visual quality, we use three stan- dard metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID). Ta PSNR, SSIM, and FID results for the ArEnAV dataset. PSNR and SSIM measure pixel-level and structural sim- ilarity, respectively, between fake and original frames (higher is better) (ArEnAV lies nearby IM). FID assesses realism by comparing the distributions of fake and real frames in a learned image feature space (lower is better) (ArEnAV slightly more than AV-IM). These scores highlight that ArEnAV attains higher / comparable visual quality compared to other deepfake datasets.
Table 4: Visual quality compari- son across different datasets.
PSNR(dR) SSIM
FIDL
FF++
24.40
0.812
1.06
DFDC
FakeAVCeleb
29.82
0.919
2.29
LAV-DF
33.06
0.898
AV-Despfake
39.49
0.977
0.49
ArEnAV
37.70
0.971
0.68
Real Perturbations: To mimic real-life video scenarios better, we add localized perturbations to both the real and the fake videos. We apply 15 different visual filters (eg, salt-pepper noise and camera shaking) and 10 different audio manipulations (eg, time-stretching. random loudness and pitch). For each video, we randomly sample one three instances for visual perturbations and one to two instances for audio perturbations. Perturbation details are mentioned in Appendix A.4
3.4
USER STUDY
To investigate whether humans can identify deepfakes in ArEnAV, we conducted a user study with 19 participants, out of which, 15 are native Arabic speakers, and 4 have basic knowledge of Arabic. We randomly sampled 20 videos, with either O or manipulation. Instructions for User Study:
Table 5: Detection and Localization results from our User Study.
Method Acc. AP@0.
AP@05
AR@
ArEnAV 60.00
8.35
0.79
1.38

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Each participant was asked to 1) watch the video, and 2) answer questions, i) Is the video real of fake, ii) it is fake, localize where they think the fake region is, iii) Whether the given video contains Arabic-Enghhn English code-switching or not, iv) Give reason for labelling the video (if they have) as a deepfake.
hypothesis that identifying audiovisual deepfakes in multilingual (specially CSW) and multimodal settings is non-trivial task, as even humans achieve only 60% accuracy, while it even harder to localize the deepfakes, with AP@0.5 0.79. Further, Table fable/Jshowss shows the primary reasons why people classified the videos as fake. We report that 85% of the users fail to identify deepfakes when the manipulation happens in the English word, in the CSW video, which can be attributed to a higher quality of voice cloning in English well as the natural change in tone when a person code-switches, which makes harder to detect. Further, localization is very tough due to the very high quality of lip-syne with diffusion models, as shown in Table 6 where the video being the reason for fake classification is only 8.7%.
The results in T
Table Distribution of top reasons for predicting video as Fake in our User Study.
Percentage (%)
Unintelligible speech (weird audio)
36.5
Video/andio mismatch (lip syne is off)
25.1
Audio sounds artificial
24.7
Video looks artificial
Codee switching is unnatural sentence
3.0
3.5 DATASET STATISTICS
Table Data distribution in ArEnAV and comparison with other multilingual datasets.
#Unique Videos
#Real Videos
#Fake Videos
#Non-English Clips
#CSW Videos
#Arabic Videos
Arable
Subset
Variants
PolyGlofFake Hou al 2024
766
14,472
11,941
1,403
NA
Illusion (hakkii pla 2025
141,440
1,234,931
4,385
0
NA
ArEnAV-Teai
6,117
67,600
202,800
270,400
69.544
200,856
Egyptian,
WVEnAV-Validation ArEntV-Test
876
9,560
28,680
38,240
10,416
27.824
MSA.
19,008
78,432
19,832
58,600
Levantine, Gulf
ArEnAV (total)
8,809
96,768
290,304
387,072
99,792
287,280
Table 7 compares ArEnAV with other multilingual deepfake detection datasets. Existing multilingual datasets like PolyGlotFake (Hou et al., 2024 and Illusion (Thakral et al.) 2025) have significantly smaller multilingual content, containing limited Arabic data (1,400 Arabic videos in PolyGlotFake and minimal in Illusion across 26 languages). ArEnAV includes 387k videos sourced from 8,809 unique YouTube videos, totaling over 765 hours. Videos average approximately 7.7 sec each, with train, val, and test splits created via multilabel stratified sampling 7:1:2 ratio, ensuring overlap.
Table 8: Fake segment duration comparison between ArEnAV and AV-
Dataset
Mean (6)
Median
Minimum
Maximum (8)
Viden-length (5)
Fake ratio (%)
Relative length
(6)
(8)
(x)
ArEnAV
0.696
0.625
0.02
6.16
5.97
12.1
2.1
AV-IM
0.326
9.07
3.7
1.0
Fake Region Comparison:: In Table g summarize forged-segmen duration statistics for ArEnAV and AV-IM. The Table highlights substantially longer and proportionally larger forged spans in ArEnAV (Fake segments are 2.1 times longer relative to AV-IM), confirming that performance drops stem from the intrinsic difficulty of detecting linguistically precise intra-utterance manipulations rather than from short spans.
We spent around 50 GPU hours to generate the real transcript using Whisper- Large-V2 V2 (Radford al., 2022), 200 dollars worth of OpenAl credits, generate fake transcripts and Text-to-Speech model, TIS-1 (OpenAl 2023), and 650 GPU hours for video generation. Overall, we needed 800 GPU hours to generate AvEnAV with NVIDIA RTX-6000 GPUs.
Computational Cost:
4 BENCHMARK AND METRICS
We organize the data into train, validation, and test split. We multilabel stratified sampling to divide the data in equal proportions based on the method type, the change mode, and the ground truth language. We also show evaluation two subsets, subset which excludes videos with audio-only
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Table 9: Temporal localization results on the test set of ArEnAV.
Set
Method
Mod.
AP@0.5
AP@0.75
AP@0.
AP@0.95
AR@
AR@30
AR@20
AR@10 AR@5
Full dataset
Meso4
V
0.02
0.01
0.00
0.00
0.09
0.09
0.09
0.09
0.09
MesoInception
V
0.56
0.18
0.04
0.01
4.11
4.11
4.11
4.11
4,08
Xccption
V
22.50
10.26
2.29
0.58
19.13
19.13
19.13
19.13
19.13
BA-TED (ZS)
AV
0.17
0.01
0.00
0.00
9.72
5.20
3.07
1.46
0.73
BA-TFD+ (ZS)
AV
0.11
0.00
0.00
0.00
5.77
2.95
2.09
0.87
0.37
BA-TFD
AV
2.42
0.55
0.01
0.00
22.30
10,31
3.41
2.54
1.67
BA-TFD+
AV
3.74
1.10
0.06
0.01
30.75
9.42
4.55
3.05
1.83
Set V
Meso4
0.02
0.01
0.00
0.00
0.10
0.10
0.10
0.10
0.10
Mesolnception
V
0.83
0.27
0.05
0.01
5.56
5.56
5.56
5.56
5.53
Xception
32.76
3.30
27.78
27.78
27.78
27.78
27.78
BA-TFD (ZS)
AV
0.12
0.00
0.00
0.00
8.44
4.34
2.44
1.13
0.49
BA-TFD+ (ZS)
AV
0.00
0.00
2.39
1.65
0.69
BA-TFD
AV
3.65
0.01
0.00
25.31
9.03
3.64
2.34
1.64
BA-TFI>+
AV
0.02
13.21
3.05
2.05
S
Mos04
V
0.02
0.01
0.00
0.00
0.08
0.08
0.08
0.08
0.08
MesoInception
V
0.38
0.09
0.01
0.00
3.25
3.25
3.25
3.25
3.22
Xception
V
14.72
3,92
0.29
0.09
11.78
11.78
11.78
11.78
11.78
BA-TFD (ZS)
AV
0.23
0.01
0.00
0.00
12.14
6.46
3.85
1.83
0.95
BA-TED+ (ZS)
AV
0.14
0.01
0.00
0.00
7.32
3.79
2.69
1.13
0.48
BA-TED
AV
3.21
0.60
0.02
0.00
24.45
9.26
4.15
2.61
1.93
BA-TFD+
AV
4.35
1.10
0.10
0.00
28.35
11.23
4.85
3.11
2.00
manipulation, and subset A, which excludes videos with visual-only manipulations. We evaluate models on two tasks, temporal localization and detection of audio-visual deepfakes. We use average precision (AP) and average recall (AR) metrics prior works (He ct al. 2022 Cai al. 2022 2023a) for temporal localization. For the task of deepfake detection, we use the standard evaluation protocol
(Rossler et al. 2019 Dolhansky et al. 2020b Cai al. 2023a) to report video-level accuracy (Acc.) and area under the curve (AUC).
We benchmark temporal detection using SOTA models: Meso4, Mesoln- ception4, Xeeption, BA-TED, and BA-TFD+ BA-TFD and BA-TFD+ (Cai et al.) 2023b) are evaluated in their original configurations, both zero-shot setting (pre-trained on AV-IM: (Cai iet al. (202aa)) and after fine-tuning on our dataset. For image- based classifiers: Meso4, Mesoln- ception4 (Alchar al. 2018); and Xception (Chollet 2017). we aggregate frame-level predictions to segments following Cai et al. (2023a). For benchmarking deepfake detection, image-basee models (Meso4, Mesolnception4, and Xception) are trained on video frames with corresponding labels, and predictions are aggregated to video-level using max voting, suggested by Cai et al. (2023a). Additionally, we assess zero-shot performance of LLM-based models, VideoLLaMAz and VideoLLaMA2.1-4 (Zhang et al. 2023), prompting them to produce confidence the likelihood of a video being a deepfake. We include an audio-only baseline, ...Baaaamba (Xiao) Das 2025). the best open-source audio deepfake detection model Speech DF Arena (Face 2025). evaluating it both zero-shot mode (pre-trained on ASVSpoof-2019 (Wang et al. (0200) and
Implementation Details:
indicating
after training with video-level labels from our dataset. BA-TFD and BA-TFD+ (Cai al. 2022
also evaluated using segmentation proposals treated as frame-level predictions and aggregated by max-voting, both pre-trained on AV-IM and fine-tuned on our dataset. For all finetuning runs, subsample the frames so as to remove class imbalance.
RESULTS AND ANALYSIS
Audio-Visii Temporal Deepfake Localization. The results for temporal localization are shown in T SOTA methods show significantly lower performance on ArEnAV as compared other localization datasets (refer to Taa) BA-TFD and BA-TFD+, pretrained on AV-1M, show drop performance more than 35% for AP@0.5 threshold, compared to evaluation on AV-IM. The image-based models, Meso4 and MesoInception4, also provide low performance, which can be attributed to the use of diffusion based lip-syne models, which have been overlooked in previous data generation pipelines (Cai et al.) 2023a Through this benchmark, claim that the highly realistic multimodal multilingual code-switched fake content in ArEnAV will open an avenue for further research temporal multilingual deepfake localization methods.
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Table 10: Deepfake detection results on the test set of ArEnAV.
Label Access
Pretraining Data
Methods
Mod.
Fullset
Subset
Subset A
For Training
AUC Acc.
AUC
Acc.
AUC Acc.
Zero-Shot
ASVSpoof-19
KLSR-Mamba
A
39.19 52.77
52.73 40.68
52.50 42.59
Internet Scale
Vidco-LLLMA (7B)
V
51.48
26.29
51.47
34.21
51,43 34.18
Internet Scale
Video-LLaMA (7B)
AV
48.79 59.29
48.71
55.37
48.86 55,26
AV-1M
BA-IFD
AV
61.73 26.00
66.42
34.07
59.36 33,97
AV-IM
BA-TFD+
AV
60.96
25.84
64.49
34.28
59.44 33.80
Vidco Level
ArEnAV
ALSR-Mambi
A
173.00 61.00
31.00/57.47
57.47
66.16
86.33 78.00
ArEnAV
Meso4
V
49.30 75.00 49.15
66.67 49.30 66.67
ArEnAV
Mesolnception4
50.34 46.23
50.28
47.48 50.35 47.67
ArEnAV
Xception
75.00 50.05 V
49.90
66.67
50.32 66.67
Frame level
ArEnAV
Meso4
V
49.55
26.60
49.60
34.40
49.53 34.36
ArEnAV
Mesolnception4
V
51.14 41.25
50.77
51.84 45.28 44.09
ArEnAV
Xception
V
74.21 52.09
85.36
67.22 68.59 51.70
AV-IM & ArEDAV
BA-TFD
AV
75.91
44.31
77.64
58 58.29 72.21 45,21
AV-IM & ArEnAV BA-TFD+
AV
79.97
27.44
84,20
36.47 72.89 34.56
Table 11: (a): Temporal localization comparison on ArEnAV, IM and LAVDF. (b): Cross-Dataset
comparison (% AUC) of recent SOTA models.
(a) Cross-dataset Deepfake Localization.
(b) Cross-datasel deepfake detection, P: DFDC-P set.
Method
AP80.5
AP
AR
AR
Method
Venue
ArEnAV
DEDC
FF++
CelebDD
LAV-DF
79.15
0.24
64.18
Capsule-w2
ICASSP-19
49.15
BA-TED
AV-IM
57.37
0.02
45.55
30.66
Faos-X-Ray
CVPR-201
55.56
98.52
ArEnAV
2.42
0.01
22.30
2.54
LipRorensics
CVPR-21
49.76
73.50
97.10
82.40
M2TR
ICMR-22
50.12
99.92
LAYDD
BA-TFD+
AV-IM
44.42
0.03
48.86
34:67
LAA-Net
CVPR-24
50.04
86.94(P)
99.95
ArEnAV
3.74
0.04
30.75
3.05
ForensiesAdaptor
CVPR-25
50.58
94,00
Audio-Visual Deepfake Detection. The detection results are in Table 10 Image based models, that have access to video-level labels only, perform considerably worse, except XL.RR-Mamaa which is designed to be trained video-level labels for audio-deepfake detection. The best performing model BA-TFD, pretrained on AV-IM and then further fine-tuned on our dataset, with AUC Score of 82% on the full subset. We also evaluate models subsets V and A, as described in the implementation details. The audio-only model, XLSR-Mamba, performs better in the Audio-only subset A, while the image-only models perform better on Subser V for frame-level labels, compared to the fullser. XLSR- MLSR-Mamba performs relatively worst when the audio is code-switched, compared to only Arabic.
Cross-Datase Comparison for Deepfake Localization. Table Ila shows the performance of BA-TFD and BA-TFD+ (Pretrained on AV-IM) on LAVDF, AV- 1M and ArEnAV datasets. Both models perform significantly worse on ArEnAV, highlighting the poor generalizability (while generalizes to LAV-DF) in multilingual and code-switching settings. BA-TFD and BA-TFD+ fail to generalize effectively, as the pretrained audio and video encoders struggle with -ditt--dittiitttionn data encountered in both modalities of ArEnAV.
Predicted
Encrgy
M myy www.yyypeammmm fr
M Myy
mmm
Ow Man
m
o) Max- Scone: 0.545, correct
b) Max Score: 0,641, correct
c) Max-Score: 0,430, wrong
d) Max-Score: 0,113, wrong
Figure 4: Different output cases from evaluation of BA-TFD+ after finetuning on ArEnAV. Here, the ground truth of all samples FAKE. Max- Scorr refers to the maximum score assigned to a candidate range during prediction. Correct means that the predicted class matches the ground truth class. The green region refers to the ground truth fake- segeee,gment, and the red region refers to the predicted segmee,,, based the Max Score. a) Shows the model predicting the correct class, along with some overlap with the ground truth fake segment. b) Shows the model predicting the correct class, but with overlap. c) and d) Show the model predicting the wrong class in the output.

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Published as a conference paper at ICLR 2026
Code-switee phrase
Predicted fake segment
Energy trend
rld yulterrdl Ma
M Mennor Myipetn
M mnn M.mm MMm
a) Mar--ccrre:aa 2543
b) Maxe-Scrre 0.504
c) Maxx- 0.573
4) Score: D.618
Figure 5: Different output cases from evaluation of BA-TFD+ after finetuning on ArEnAV. Here, the ground truth of all samples is REAL. Max-Score refers to the maximum score assigned to candidate range during prediction. The green region refers to the code-switching region in the real video, and the red region refers to the predicted fake-segment. a), All samples show that the model misclassifies real videos as fakes, specifically at the code-switching regions.
Cross-Dataset Comparison for Deepfake Detection. TTb shows the cross dataset performance of recent SOTA deepfake detection models, including Capsule-v2 (Nguyen et al. et al. 2020a LipForensies (Haliassos et al. 202T) and M2TR (Wang et al.
2019),
Prce-X-Ray
2022 All models
were pretrained on FaceForensics++ (Rössler et al.) 2019). While models show great cross-dataset performance on DFDC and CelebDF, they fail pertorm better than guessing (50% AUC) on ArEnAV. Even recent SOTA models, such as ForensicsAdaptor (CVPR-25) (Cui et al. (025b) and LAA-Net (CVPR-24) (Nguyen et al., 2024). fail to generalize. The demographic and linguistic homogeneity of existing datasets (FF++, CelebDF, DFDC) limits model robustness. By incorporating multilingual audio and broader participant diversity, our dataset demonstrates why architectures must be designed to generalize beyond those biases.
6 QUALITATIVE ANALYSIS
Figures 4 and show qualitative examples for different output cases of BA-TFD+, finetuned on ArEnAV. Figure iillustrates model predictions on fake samples, showing four representative cases comparing predicted fake segments with ground-truth regions. some cases, the model correctly identifies the manipulated class with good temporal overlap, while in others, the predictions are either misaligned or incorrect. Importantly, these outputs demonstrate that the model's behavior is not influenced by any boundary-leve artifacts splicing cues introduced during data generation. Further, spectrograms from BA-TFD+ outputs show no abrupt energy shifts or discontinuities near edit boundaries, indicating the absence of splice artifacts the boundaries of manipulated content.
Figure 5 shows the model's predictions on real code-switched videos. Here, the model frequently misclassifies real videos fake, primarily due to the presence of code-switching between Arabic and English. The high predicted fake scores at these regions indicate that the model confuses natural linguistic transitions with synthetic inconsistencies. Together, these qualitative results confirm that the challenge in ArEnAV arises from the intrinsic complexity of code-switching rather than from generation artifacts.
CONCLUSION
This paper presents ArEnAV, large multilingual and the first code-switching audio-visual dataset for temporal deepfake localization and detection. The comprehensive benchmark of the dataset utilizing SOTA deepfake detection and localization methods, indicates significant drop in performance compared to previous monolingual datasets, indicating that the proposed dataset an important asset for building the next generation of multilingual deepfake localization methods.
Limitations. Similar to other deepfake datasets, ArEnAV exhibits a misbalance in terms of the number of fake and real videos. Due to the limited performance of current SOTA Active- Voice-Detectio (Whisper v2) models on Arabic (compared English), the data generation pipeline can result in a few noisy transcripts. Due to limited instruction following in code-switching scenarios, LLMs might not produce the desired results, as visible in Figure 3 "Meaning + Translation Scenario". Compared to other subsets, Chat-GPT often fails to follow both instructions, making real and fake transcripts too similar and not always changing their meaning. Also, the dataset is currently limited to two languages only, where we hope motivate further research in this direction.
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Broader Impact. ArEnAV's ' diverse and realistic English-Arable fake videos will support the devel- opment of more robust audio-viste deepfake detection and localization models, better equipped to handle code-switched speech and real-world multilingual scenarios.

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Published as a conference paper at ICLR 2026
8 ETHICS STATEMENT
Our work on ArEnAV raises important ethical considerations, especially given the sensitivity of deep- fake research. The dataset is built from publicly available YouTube content, in line with established practices in benchmarks. Use of such material for non-commercial research is covered under fair use, and access to ArEnAV is gated by a strict End-User License Agreement (EULA)(Section) A.7 Below, we detail all the ethical considerations regarding our work:
Use of YouTube Videos: The ethical foundation of our data collection does not rely on VisPER but on established practices in prior peer-reviewed datasets such as LRS3-TED (Afouras et al. 2018b), VoxCeleb2 (Chung et al. 201 and AVSpeech (Ephrat et al., 201 8a). which employ the same keyword- and detectenn pipeline. We build our dataset from public YouTube videos under the research-focused "fair use" exception established in peer-reviewed work (e.g., Zhu et al. (2024)). Access is granted only after users agree to our EULA, which lists the following rules and regulations:
Access will be granted only to researchers who supply their university IRB application ID, and every project member must use an individual account, safeguarding traceability and preventing misuse.
Users are eligible to conduct independent research at their respective institutions and the Institution accepts responsibility for its Authorized Investigators' actions related to the use of ArEnAV.
Limits use to academic, non-commercial, not-for-profit research and education.
Authorizes licensors to modify the data or license at any time and prohibits licensees from altering the database.
Forbids any use that could cause subjects embarrassment or mental anguish.
This approach accords with established practice across the community, as evidenced by DF40 Yan et al., 2024), which draws real videos and images from FaceForensics** (Rössler et al. 2019).
Celeb-DF (Li et al. 2020b), CelebA (Liu et af. 2015). FFHQ (Karras et al. 2019), and VFHQ Q (Xie
et al. 2022); DeepfakeBench (Yan al., 2023). which relies FaceForensics* and Celeb-DF
Foensssrns:::+ Celeb-DF; FakeAVCeleb (Khalid et al. 202I), which builds on VoxCeleb2 (Chung al. (01 AVLIPS (Liu et al. 2024). which sources from LRS3 (Afouras et al. 201 2018a) and FaceForensicct+: and AV-IM (Cai et al. 20246), which is derived from VoxCeleb2. Together, these
measures and precedents demonstrate that curating public YouTube content for commerciaal scientific inquiry is responsible and widely adopted practice.
Face Detection techniques applied on videos: We acknowledge the risks of working with videos that contain faces, but face detection is used only as preprocessing step and not for identification. In line with recent peer-reviewed works, using videos containing faces, for different research problems that involve face detection as a common prior step, is standard practice, e.g.: a) LRS3-TED (Afouras) ct al. (018b)), VoxCeleb (Nagrani et al. 2017), VoxCeleb2 (Chung et al. 201 8b)) have been used for Speaker Identification, Verification, Recognition, and further, for Deepfake benchmark creation (AV-IM (Cai et al., (2224)) and FakeAVCeleb (Khalid et al.] (022)) b) MultiTalk (Sung-Bir et al. 2024) uses videos from YouTube for Talking Head generation c) AVSpeech (Ephrat al. 2018b) used for Speech Separation d) Hallo3 (Cui et al. 2025a) used for Portrait Image Animation.
Since it is impractical to get individual consent for open-source content, mitigate misuse by requir- ing institutional IRB approval, individual researcher accounts for access and a removal mechanism to request the removal of personal content.
Human Study: Our human study followed university IRB guidelines: participants were over 18 years of age and were approached over email through connections in research groups within the affiliated universities of the authors. The participation was strictly voluntary and anonymous. All the details about the research project and conditions for participation in the study were clarified through an Explanatory Statement at the beginning of the user study form. Thus, the users consented to participate in the study by filling out and submitting the study form (Google form), and all material was screened to avoid disturbing content. No personal data were recorded and no compensation was provided.
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9 REPRODUCIBILITY STATEMENT
Our data will be open-sourced. Data-generation code and evaluation scripts will be made public for various open-sourced models evaluated.
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A APPENDIX
A.1 IDENTITY ANALYSIS IN ARENAV
We conducted face analysis using InsightFace and DBSCAN to better understand potential identity overlaps across dataset splits. While each split (train, validation, and test) already contains indepen- dent YouTube video IDs, we found a 41.7% identity match between the combined train+val and test sets at a similarity threshold of 0.7.
To verify whether this overlap influences model performance, we evaluated BA-TFD and BA-TFD+ separately on overlapping and non-overlapping subsets. As shown in Table [2 the results remain consistent across both groups, indicating that identity overlap does not affect the models' behavior. We will release separate identity- test set in the final version to further strengthen reproducibility and fairness.
Table 12: Performance of BA-TFD and BA-TFD+ models on ArEnAV test set. Each column shows Accuracy@00 @05 and AUC, with the number evaluated videos in parentheses.
Model
Overall (78,400)
Non-Overlapping (45,512)
Overlapping (32,888)
Acc
AUC
Acc
AUC
Acc
AUC
BA-TFD+
27.44
79.97
27.58
79.96
27.31
79.99
BA-TFD
44.31
75.91
44.35
75.96
44.27
75.85
A.2 AFFECT LIMITED INSTRUCTION FOLLOWING OF GPT-4O-MINI FOR "MEANING + TRANSLATION" TASK:
some cases, the "meaning + translation" mode produced sentences that were semantically similar to the originals. This can be improved by reprocessing the LLM outputs and filtering based on entailment or cosine distance to enforce greater semantic variation. However, this does not affect our main conclusions. The detection task in ArEnAV focuses on identifying audio-visual inconsistencies, not the extent of semantic change.
Only small fraction of the data (less than 8,000 out of 280,000 samples, and about 2,000 of 78k in the test set) (Figure 31)) has an entailment quality mean > 0.8.
To reaffirm this, compared the best pest-performing models across these subsets. As shown in Table 13 the performance remains consistent, indicating that these few cases do not lower the dataset's overall difficulty.
Table 13: Performance of BA-TFD and BA-TFD+ models ArEnAV test Each column shows AUC scores.
Model
Overall
entailment<0.8
entailment>0.8
BA-TFD+ 79.97
79.98 79.96
BA-TED 75.91
75.92
75.89
A.3 AFFECT OF DIFFERENT LIP-SYNC AND TTS MODELS ON THE PERFORMANCE:
Table 14 reports model accuracy across four TTS and 2 lip-sync generation methods (finetuned on ArEnAV). For audio-only detection, XLSR-Mamba achieves the highest accuracy with Fairsey-OpenVoice (95.8%), indicating that lower-quality TTS outputs more easily detected. Voice Higher-fidelity systems such as XTTS-Open Voice and XTTS-v2 reduce detectability, suggesting improved synthesis quality. For audio-visual models (BA-TFD and BA-TFD+), both Diff2Lip and LatentSync yield similar accuracy levels, confirming stable and high-quality visual performance. Overall, the results show that better generation quality leads to lower detectability, aligning with our goal of creating challenging, realistic benchmarks.
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Table 14: Accuracy across different Audio and Video generation models used in ArEnAV.
Audio Modiels (Subsett A)
Video Models (Subset-V)
Model Type
Fuirsey Open
GPT-TTS
Open
Dif12lip
Latensync
95.8
78.8
19.4
77.9
38.6
42.8
BA-IFI ATF+
69.5
58.9
68.9
31.7
32.9
BF TF-D
33.7
32.2
29.3
31.9
58.1
56.2
A.4 REAL PERTURBATIONS
Table 15: List of video and audio perturbation types with descriptions.
Category
Perturbation Type
Description
Gaussian Blur
Applies Gaussian smoothing to simulate out-af-focut capture.
Salt and Pepper Noise
Random white and black pixel noise, mimicking sensor errors.
Low Bitrate Compression
Blocky, artifact-heavy images due to compression.
Gaussian Noise
Electronic sensor noise typical in low-light conditions.
Poisson Noise (Shot Noise)
Noise from photon-limited imaging environments. Multiplicative noise creating granular interference effects.
Speckle Noise
Color Quantization
Banding effects from limited color palettes.
Video Perturbations
Random Brightness
Simulates variations in exposure and lighting. Imitates camera ohject motion during capture.
Motion Blur
Rolling Shutter
Distortion effects due CMOS sensor movements.
Camera Shake
Minor frame shifts from handheld camera vibrations.
Lens Distortion
Optical distortions like barrel or pincushion effects. Darkening of image edges typical of certain lenses.
Vignetting
Exposure Variation
Adjusts brightness and contrast, simulating exposure issues.
Chromatic Aberration
Color channel shifts causing fringing effects.
Compression Artifacts
Quality loss from low bitrate compression.
Pitch/Loudness Distortion
Gain frequency alterations simulating recording issues.
White Noise
Constant background electronic interference noise.
Time Stretch
Audio speed adjustments without pitch change.
Reverberation
Echo reverb modeling room acoustics.
Audio Perturbations
Ambient Noise
Background environmental sounds added.
Clipping
Distortion from excoeding audio amplitude limits.
Frequency Filter
Filtering effects simulating transmission equipment variations.
Doppler Effect
Pitch modulation due relative motion.
Interference
Static-like bursts mimicking external disturbances.
Room Impulse Response
Complex echo patterns modeling specific environments.
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A.5 AUGMENTATION EXAMPLES
In Table I6 we provide examples of augmentations achieved through the manipulation rules previ- ously outlined in Section 3.2.1
Table 16:
Examples of augmentations achieved through the different transcript manipulation rules, showing the original (orig) and augmented (aug) transcriptions.
Original
Original
Inserted Word
Transcription
Word
Operation
Example
Edit: Telephone Radio
Telephone Ji بf اليهم التعك [orig]
CSW
EN
EN
Change meaning only (keep English)
(The lady got busy on the telephone)
Radio 3 الوام ciued [aug]
(The lady got busy with radio)
Edit: s ماكر (MSA) مدووة (MSA)
( i متتر لميحت Mirroring J [orig]
CSW
AR
AR
Change meaning only (keep Arabie variant)
(Mirorring become a popular tool)
G مدلودة أiál اممهت Mirroring [aug] (Mirorring has become a limited tool)
Edit: بلر (MSA) - fuf: (Dialectal Arabic)
0ely اييي sponsors J Js بفو [orig]
CSW
Change mesning change Arabic variant
(I thank present sponsors)
Selyeuse sponsions Jl JS A [aug]
(1 hate all the present sponsors)
Edit: -n-- (MSA) - حزيز (MSA)
س اطسي المخم محكةة [urig]
Arabic
AR
AR
Change meaning only (keep Arabic variant)
(And this person will content and happy)
ش أحس النخص is مبكرت [aug]
(And this person will be content and sud) Edit: o$ م (MSA) - (Dialcctal Arabic)
و ممم للسيي بملل ششر كات [orig] (She was involved in and essential task)
Arabic
AR
AR
Change meaning change Arabic variant
4i4 اساسي Jon dl اeر cd [aug]
(She was involved a core and non-essential task)
Edit: اللل friends
النان قنر بريح Uا [orig]
Arabic
EN
EN
Change mcaning
(1 go meet people)
change language to English
friends قلا ب u [aug] go friends)
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A.6 PROMPT FOR TEXT PERTURBATION
Prompt for Fake Transcript Generation.
##SSYSTEM MESSAGE##
You are controlled text pextuueitur bot. Here is the transcript of an audic. Please use the provided operations to modify the transcript to change The operation can be one of "delete', "insert" and 'replace'.
sentiment.
Please priority modify adjectives and adverbs. C
meaning_only
Change the *meaning* of one word.
Keep the same language/script and dialect.
dia Bialect_one dialect_only
Swap word for a dialectal equivalent of videntical meaning
Example: <syArT> <byyy>> (Gulf dialect, same meaning).
dialect maauing
Change *both+ dialect +and* meaning in a single word. Example: <jmyl> (msa, 'nice') <wH$S> (Egyptian, 'awful'),
meaningg_plue_ttrasalatta
In Arabic-only sentences, pick a word that is *ommmonly code-switched
to English** in everyday speech (e.g., <mwbayl>, <syArT>, <Antrnt>). Translate that word to English and change the meaning simultaneously.
Example: <syArT> ('car") bike.
- MULTI-OP LOGIC---
If language -- 'csw';
num edit exactly one token matching target_token_serjpt. num 2 edit English + Arabic taken. num = 3 edit English + 2 Arabic takens.
-TTHER
Only modify tokens that *commenly code-switched- in real speech (brand names, technology, everyday neuns, etc.).
Each operation targets ONE word (delete / insert / replace).
Number of operations for INSERT, DELETE and REPLACE
should be equal across
the data.
If sentiment can be changed with INSERT or DRLETE, prefer it over REPLACE.
When dialect shifts, include original dialect and new new dialect.
Never alter tense add restricted content.
Return **only** JSON object that matches the schema.
Figure 6: System prompt for tertuperttrbation bot
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A.7
END USER LICENSE AGREEMENT (EULA FORM)
End User License Agreement.
End Usez License Agresment
(Asadenic, non commercial, not for profit licence) Cupysight (e) 2025 [AOTHORS]
rights reserves.
The goel of the ArÊnAV database is to develop new technigues, technology, and algorit ims Zue inndin, gude suitched despfake delection and localixat iun, as most of Lhe wxiating Lag reaearch wy Tocuses nanelingual content, ofter overlooking the challenges of mulcilinqual and The licenzora
within the ase invalved an ungping wffus. to aczengthen delection algoritims againat highly realiatic deeptakes, The dataset is neant to aid pesearch efforts in the gensral area of developing, testing and evaluating algorishna for multilingual code suitched deepfake det ior and localizalion.
ewittched speech,
miltiple languages
To receive a copy the detaset, the requester must egree to obeerve the condit conditions Hattd ow.
The geal of the ArEnNV database is to develop new techniques, technology, and olgorithna for presict ing and locating beith tinestamps) where video has been nanipulated, culaaryl
Arabic English uode awitching. databases and amccaation above aburce and binasy fozn, provided that the following conditions are nat:
pernitted of the
The databaze is provided under the terns of this liccace strietly for academic, 3on-comeercial, not-ter-grocit purpases, Requestoz needs supply their university IRB application ID, every project nanber individuel account, sefeguarding tracmability and preventing misuxe. Atsach the 138 approval in + the smail along with the signed BULA fosn.
Rediatribution, sapiblishing, or dissenination in any forn, souzce binary, is not permitted licmneor, Linking
xithout
written approva: by HERE] is pernittes.
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The namees of the Eccessos mey not be used to enderse or promote products
der ived from this softvare without speci fin prior writter pernixsion.
Tae licensers reserve the right to modity the data/License at any point.
Moditicstion the database licensees is perniteed.
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still
any wey
original
subfect cabaeta oe sental angulsh.
You anderatand thas the AeBrAV datase: is a deeefake dataset generaced based on ViaPer 112406.00300 Vispel: bilt:lingal I Audio-Vizual Speech Recognitions datase:/s krabic Train subset. You also agrew to all agreementy of the ViaPer
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signed
Print
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Published as a conference paper at ICLR 2026
A.8 LLM USAGE
Along with the use of Large Language Models (LLMs) as described in our Data-Creation process, we made limited use of LLMs enhance the clarity and readability of the text. They were not involved in the conception of ideas, the design of experiments, analysis, or the production of results.
24


Reviews:
[{"review_id": "vFrzlv6c18", "reviewer": "Reviewer_W12a", "summary": "This paper introduces ArEnAV, the first large-scale Arabic–English code-switching (CSW) audio-visual deepfake dataset, consisting of 387K videos (765+ hours) with intra-utterance CSW, dialect variation, and multiple manipulation types. The authors propose a multi-stage generation pipeline combining GPT-4.1-mini-based transcript manipulation, four TTS systems, and two diffusion-based lip-sync models. The paper benchmarks several state-of-the-art deepfake detection and localization models and shows a drastic performance drop when evaluated on ArEnAV, demonstrating the dataset’s difficulty and relevance. A user study further confirms that humans also struggle to detect these deepfakes (≈60% accuracy). The dataset and code are promised to be released.", "questions": "1.\tCould the authors clearly articulate which parts of the pipeline are technically novel, and whether it is reusable beyond this dataset?\n2.\tIf these APIs change or become unavailable, can the dataset still be regenerated?\n3.\tDid the authors run any linguistic validation (human or automatic) to ensure the generated CSW resembles real corpora like ZAEBUC or ArzEn?\n4.\tCan the authors provide quantitative evidence on how different TTS/lip-sync components affect detectability or quality?\n5.\tHow does this imbalance affect model training? Did the authors experiment with balancing, reweighting, or sub-sampling?\n6.\tCan the authors comment on whether the pipeline could scale to other CSW settings (e.g., Hindi-English, Spanish-English)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.\tExisting deepfake datasets are monolingual or multilingual but lack intra-utterance code-switching. The paper clearly identifies this gap and addresses it convincingly.\n2.\tLarge-scale, well-engineered dataset. 387K videos, 4 TTS + 2 lip-sync models, stratified splits, strong statistics, and detailed generation pipeline. The dataset is significantly larger and more diverse than prior multilingual datasets. The authors show that state-of-the-art models (e.g., BA-TFD, LipForensics, Capsule-v2) perform poorly, even close to random guessing on some settings, demonstrating real difficulty.\n3.\tHuman performance ≈60% accuracy, poor localization ability → confirms that deepfakes in code-switching settings are hard even for humans, not only for models.", "weaknesses": "1.\tThe paper does not propose any new detection model or algorithm. I feel the work as “engineering + dataset release” rather than a scientific advance.\n2.\tHeavy reliance on closed-source models (GPT-4.1, Whisper, TTS-1, etc.). Reproducibility is partially limited. If OpenAI APIs change, future users may not be able to regenerate the dataset. This may be flagged in the reproducibility checklist.\n3.\tAlthough CSW is the main motivation, the paper lacks deeper linguistic validation:\n⦁\tIs the LLM-generated code-switching natural vs synthetic?\n⦁\tHow does the CSW distribution compare to real-world corpora?\n⦁\tDoes GPT-4.1 make linguistically plausible switching decisions?\n4.\tReal vs fake imbalance is acknowledged but not studied. No experiments showing how class imbalance affects model learning. Meanwhile, generalization to other multilingual settings not demonstrated.", "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": "deiM3vPmCg", "reviewer": "Reviewer_8ZWD", "summary": "The authors in the paper point out the lack of a large-scale multilingual dataset that includes both English and Arabic, particularly one featuring code-switching between the two languages. They emphasize that code-switching is highly prevalent in daily speech across the Arabic world. To address this gap, they introduce the first large-scale Arabic–English audio-visual deepfake dataset, which features intra-utterance code-switching, dialectal variation, and monolingual Arabic content. It contains 387k videos and over 765 hours of real and fake videos generated by the up-to-date SOTA model.", "questions": "1. Why was there no intra-dataset evaluation for the necessity of code-switching manipulation?\n\n2. Could you provide a few qualitative examples (e.g., video frames or audio spectrograms) of your fine-tuned models' failure cases? We need to see where the models are failing. Are the incorrect predictions concentrated around the temporal boundaries of the code-switch? Or are they failing due to other artifacts (like the lip-sync) that are unrelated to the CSW\n\n3. Table 10a shows comparison with the proposed partial fake dataset with full fake dataset, is it fair comparison? \n\n4. Did the author make the dataset identity disjoint? Also does the dataset contain real and fake pair information in metadata?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- It will be released as an open-source, large-scale bilingual dataset. Given that Arabic is spoken by hundreds of millions of people all over the world, the dataset holds significant importance. \n\n- The data generation pipeline was clearly described in the paper, enabling easy reproducibility. \n\n- The quality of the generated fake data was comparable with the well known dataset AV-Deepfake1M, as evaluated by standard metrics.", "weaknesses": "1. Insufficient Direct Experimental Evidence for the \"Code-Switching\" Contribution\n\nThe paper's central contribution is its focus on a multilingual and code-switching (CSW) dataset. However, the experimental results presented in Tables 8, 9, and 10a do not directly prove that this code-switching characteristic is the key factor driving the dataset's difficulty. The authors demonstrate that existing models perform poorly and attribute this failure to the novelty of CSW.\nSpecifically, the zero-shot performance drop in the temporal localization task (Table 8) is a weak argument. To my knowledge, models like BA-TFD were not designed for zero-shot generalization to new domains, languages, and generation methods. Their failure on this cross-domain dataset is expected and does not isolate the impact of code-switching itself.\n\n2. Absence of \"Close-Inspection\" and Qualitative Analysis\n\nConnecting to the first point, the paper lacks a \"close-inspection\" of why the models fail. The analysis relies heavily on aggregate metrics (AUC, AP), which only show that models fail, not the cause. To sophisticate the claim that CSW and multilingualism are the core challenges, a deeper analysis of the fine-tuned models is necessary.\nThe authors could provide qualitative, frame-level examples of incorrect predictions,\nBut with current state, paper cannot clearly answer the question below.\nDo the models consistently fail at or around the code-switched regions? Or are the errors more correlated with the high-quality diffusion-based lip-sync, specific audio perturbations, or other artifacts?\nIt is difficult to disentangle the source of the dataset's complexity. It is unclear whether this dataset is challenging because of its CSW properties or because of the careful, high-quality generation techniques and perturbations the authors have induced.\n\n\n\t\n3. Use of Outdated Benchmark Models\n\nThe choice of models for benchmarking (Meso4, MesoInception4, Xception) is a notable weakness. These are significantly outdated models that are widely known to overfit specific dataset artifacts and lack generalization capabilities.\nWhile BA-TFD is included for the temporal task, the overall detection model suite is quite naive. Relying on these older architectures makes it difficult to assess if the reported performance drop is due to the dataset's genuine complexity or simply the known limitations of these models. The benchmark would be far more compelling if more recent, state-of-the-art detection models with demonstrated generalization capabilities and evaluated.\n\nMinor:\nThe result for LAA-Net listed under the DFDC dataset in Table 10b is incorrect.\nThat specific performance metric was obtained on the DFDC-P (DFDC Pre-processed) dataset, as noted in the original citation by the authors.\n(See: https://github.com/10Ring/LAA-Net/issues/3)\n\n\n\n4. The protocol of the data generation pipeline is similar to the approach used in generating fake data for AV-Deepfake1M. No novel architecture for fake data generation was introduced.\n\n5. The authors did not conduct an intra-dataset evaluation to assess how code-switching affects the accuracy of temporal localization or deepfake detection models within the same domain. Instead, they evaluated models which were trained on a dataset containing a small number of Arabic videos (presumably without code-switching) and then tested on the proposed ArEnAV dataset. However, this is across domain evaluation, so the drop in accuracy is expected. \n\n6. The dataset also shows limited instruction following in code-switching scenarios; the authors relied on the GPT model to generate code-switching transcripts, making real and fake transcripts too similar and not always changing their meaning.\n\n\n7. The motivation is sound, but the authors have not considered the models such as Diff2Lip and LatentSync, are trained on English speaking videos, and the natural lip-sync to real-life Arabic speaking phonemes are different than English, including the speed of which Arabic is spoken. The authors should specify the limitations and rationale of using this.\n\n8.\tIn Table 10 training protocol is not described. As fake part can be anywhere in the video, so in this experiment the split of video clips, is not defined, also it is not written how much length of video is used to test? Similarly, the details of the experiment should be disclosed for fair comparison. \n\n9.\tThe authors used 4 commercial audio generation methods, while for visual manipulations the authors used only 2, which are also not commercial. Visual manipulation is harder to detect; hence diversity can help more generalizable deepfake detection. \n\n10.\t3.2.2 Audio generation pipeline is very messy, hard to comprehend, a flow chart could help understand this better. \n\n11. Figure 1 text is very small can be improved, also the fonts used for Arabic can be improved for better readability. \n\n12. \tAlthough language overlap is addressed, the identity split is not defined. \n\n13.\tMeta data information is not provided. \n\n14. The dataset contains imbalance samples, which may introduce bias while training.", "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": "iLkCkJgT7j", "reviewer": "Reviewer_Wf9J", "summary": "This work introduces ArEnAV, a large-scale Arabic-English audio-visual deepfake dataset, designed to address a critical gap in current research: the challenge of detecting deepfakes within multilingual and code-switched content. The work's core contributions are twofold: 1) the ArEnAV dataset itself, the first large-scale benchmark featuring intra-utterance code-switching and dialectal variations; and 2) a novel data generation pipeline that leverages Large Language Models for content manipulation and integrates SOTA TTS and lip-sync models to generate high-fidelity forgeries. Comprehensive benchmark results highlight the dataset's challenging nature, demonstrating a significant performance drop in state-of-the-art models. These findings validate the limitations of current detectors that are predominantly trained on monolingual data.", "questions": "1. The paper notes that the LLM did not always change the meaning in the \"meaning + translation\" mode. Did you try to fix this? If not, how do you know these samples did not lower the dataset's overall difficulty and affect your final conclusions?\n2. Could you provide the average, minimum, and maximum duration of the fake words in your dataset? This information is very important. Without it, we cannot be sure if the poor model performance is because the task is hard, or because your evaluation metric (AP@IoU=0.5) is simply too strict for such short fake clips.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This work successfully tackles an important and overlooked problem: detecting audio-visual deepfakes in code-switched (CSW) speech. This is a major step towards building deepfake detectors that work in the real world.\n2. This work proposes ArEnAV, a new large-scale dataset for this task. The pipeline used to create the data is novel and combines several SOTA models, providing a valuable new resource for the research community.", "weaknesses": "1. The primary evaluation metric (AP@IoU=0.5) may be poorly suited for the dataset's extremely short, single-word forgeries.\n2. The \"TTS and insert\" audio generation method can create unnatural splice artifacts, which may affect the dataset's validity. These artifacts could allow models to detect forgeries using simple audio errors rather than the intended code-switching cues, thus misrepresenting the true nature of the detection challenge.", "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)