Deepfake detection has become a fundamental component of modern media forensics. Despite significant progress in detection accuracy, most existing methods remain computationally intensive and parameter-heavy, limiting their deployment on resource-constrained edge devices that require real-time, on-site inference. This limitation is particularly critical in an era where mobile devices are extensively used for media-centric applications, including online payments, virtual meetings, and social networking. Meanwhile, due to the unique requirement of capturing extremely subtle forgery artifacts for deepfake detection, state-of-the-art quantization techniques usually underperform for such a challenging task. These fine-grained cues are highly sensitive to model compression and can be easily degraded during quantization, leading to noticeable performance drops. This challenge highlights the need for quantization strategies specifically designed to preserve the discriminative features essential for reliable deepfake detection. To address this gap, we propose DefakeQ, the first quantization framework tailored for deepfake detectors, enabling real-time deployment on edge devices. Our approach introduces a novel adaptive bidirectional compression strategy that simultaneously leverages feature correlations and eliminates redundancy, achieving an effective balance between model compactness and detection performance. Extensive experiments across five benchmark datasets and eleven state-of-the-art backbone detectors demonstrate that DeFakeQ consistently surpasses existing quantization and model compression baselines. Furthermore, we deploy DefakeQ on mobile devices in real-world scenarios, demonstrating its capability for real-time deepfake detection and its practical applicability in edge environments.
Figures & tables
Figure 1: We present the first quantization framework tailored for deepfake detection, DeFakeQ , enabling real-time deployment on edge devices. Compared to existing lightweight model designs and conventional quantization approaches, our framework significantly reduces computational cost while maintaining high detection accuracy.
Figure 2: The overview of DeFakeQ . It comprises two core components: Horizontal Adaptive Quantization (HAQ) and Vertical Efficient Feature Fine-Tuning (VEFT). HAQ first calculates the importance scores of weights within each network block. Guided by a specially designed horizontal loss function Lhor , it adaptively identifies blocks rich in discriminative information and assigns them higher bit-widths, ensuring critical forgery-related features are preserved during quantization. Complementing HAQ, VEFT operates at the channel level: it adaptively identifies and prioritizes sensitive feature channels within each quantized block. This mechanism maximizes the retention of discriminative features in quantized blocks while maintaining efficient compression of redundant channels.
Detectors
Type
Storage Cost (MB) ↓
Compression Rate (%) ↑
Detection Accuracy (%) ↑
BRECQ ICLR’22
Adalog ECCV’24
FIMA-Q CVPR’25
Ours
GT
BRECQ ICLR’22
Adalog ECCV’24
FIMA-Q CVPR’25
Ours
BRECQ ICLR’22
Adalog ECCV’24
FIMA-Q CVPR’25
Ours
GT
DeiT-Tiny
Backbones
5.9
2.8
2.4
2.2
23.7
75.0
88.2
90.0
90.7
52.1 (-17.2%)
54.5 (-14.8%)
53.2 (-16.1%)
69.3
77.8
DeiT-Small
20.2
9.3
7.6
7.5
84.1
76.0
88.9
91.0
91.1
50.3 (-21.8%)
52.8 (-19.3%)
54.1 (-18.0%)
72.1
79.4
DeiT-Base
75.9
33.0
26.4
31.7
330.2
77.0
90.0
92.0
90.4
58.6 (-15.9%)
56.9 (-17.6%)
60.3 (-14.2%)
74.5
82.3
ViT-Small
21.0
10.1
8.4
8.2
84.1
75.0
88.0
90.0
90.2
56.4 (-20.9%)
58.2 (-19.1%)
61.7 (-15.6%)
77.3
84.5
ViT-Base
79.2
29.7
26.4
31.2
330.2
76.0
91.0
92.0
90.6
57.2 (-20.9%)
58.8 (-19.3%)
61.1 (-17.0%)
78.1
86.7
Table 1: Cross-dataset evaluations of Detection Accuracy, Storage Cost and Compression Rate on testing Dataset DFD Zi et al. (2020) . All quantized detectors are trained on FF++ Rossler et al. (2019) . Our method achieves significantly improved detection accuracy (%) comparable to existing quantization approaches , while remains comparable in Compression Rate (%) and Storage Cost (MB).
Detectors
Type
CDF-v1 (Accuracy%)
CDF-v2 (Accuracy%)
DFDC (Accuracy%)
BRECQ ICLR’22
Adalog ECCV’24
FIMA-Q CVPR’25
Ours
GT
BRECQ ICLR’22
Adalog ECCV’24
FIMA-Q CVPR’25
Ours
GT
BRECQ ICLR’22
Adalog ECCV’24
FIMA-Q CVPR’25
Ours
GT
DeiT-Tiny
Backbones
50.2 (-18.3%)
55.8 (-12.7%)
54.3 (-14.2%)
68.5
76.7
45.3 (-19.2%)
47.6 (-16.9%)
49.9 (-14.6%)
64.5
73.3
37.2 (-20.5%)
39.4 (-18.3%)
40.3 (-17.4%)
57.7
65.1
DeiT-Small
48.9 (-22.2%)
51.9 (-19.2%)
55.0 (-16.1%)
71.1
78.2
42.1 (-25.9%)
50.1 (-17.9%)
50.5 (-17.5%)
68.0
74.8
36.1 (-23.9%)
40.0 (-20.0%)
42.5 (-17.5%)
60.0
66.4
DeiT-Base
60.8 (-12.7%)
58.5 (-15.0%)
61.2 (-12.3%)
73.5
81.2
46.6 (-22.7%)
48.2 (-21.1%)
49.8 (-19.5%)
69.3
77.8
42.7 (-21.4%)
41.6 (-22.5%)
47.5 (-16.6%)
64.1
71.4
ViT-Small
58.1 (-17.9%)
59.7 (-16.3%)
62.8 (-13.2%)
76.0
82.0
44.7 (-29.3%)
46.6 (-27.4%)
52.2 (-21.8%)
74.0
79.8
43.1 (-23.7%)
42.8 (-24.0%)
47.1 (-19.7%)
66.8
72.1
ViT-Base
58.9 (-17.5%)
60.0 (-16.4%)
62.2 (-14.2%)
76.4
83.7
48.2 (-26.5%)
47.2 (-27.5%)
56.3 (-18.4%)
74.7
81.9
45.2 (-22.0%)
45.1 (-22.1%)
52.4 (-14.8%)
67.2
73.6
Table 2: Additional Cross-datasets evaluation of Detection Accuracy on testing datasets CDF-v1 Li et al. (2020c) , CDF-v2 Li et al. (2020c) , and DFDC Dolhansky et al. (2019) . All quantized detectors are trained on the FF++ dataset Rossler et al. (2019) , with the best results highlighted in bold.
Dateset DFDCP Dolhansky et al. (2019) (Accuracy%)
Quantization Methods
Detectors
Type
BRECQ Li et al. (2022b) ICLR’22
Adalog Wu et al. (2024) ECCV’24
FIMA-Q Wu et al. (2025) CVPR’25
Ours
GT
DeiT-Tiny
Backbones
43.3(-20.4%)
44.7(-18.8%)
54.1(-15.1%)
63.7
72.1
DeiT-Small
44.1(-33.6%)
52.9(-20.3%)
55.1(-17.0%)
66.4
73.5
DeiT-Base
46.1(-33.4%)
55.3(-20.1%)
57.6(-16.8%)
69.2
76.8
ViT-Small
46.2(-35.5%)
55.4(-22.6%)
57.8(-19.3%)
71.6
77.0
Table 3: Additional Cross-datasets evaluation of Detection Accuracy on testing dataset DFDCP Dolhansky et al. (2019) where all quantized detectors are trained on FF++ Rossler et al. (2019) .
Methods
Venue
Technology Category
DFD Zi et al. (2020)
CDF-v1 Li et al. (2020c)
DFDC Dolhansky et al. (2020)
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
ADD Woo and others (2022)
AAAI’22
Knowledge Distillation
49.5
46.6
49.0
47.7
48.2
49.0
37.1
36.8
35.2
GAC-FAS Le and Woo (2024)
CVPR’24
Neural Architecture Search
52.1
51.3
50.6
45.1
48.9
47.6
45.1
45.0
41.3
X-Pruner Yu and Xiang (2023)
CVPR’23
Model Pruning
52.1
55.5
59.1
53.9
51.0
51.5
48.6
47.2
50.1
One-bit Xu et al. (2024)
NIPS’24
Low-Rank Factorization
56.6
53.2
52.5
50.0
48.1
47.1
40.5
43.2
42.7
HOLa Lei et al. (2025)
ICCV’25
Low-Rank Factorization
59.2
57.0
55.4
48.8
51.9
49.9
46.5
50.2
52.7
Table 4: Comparison with compression methods across other technical categories on detection accuracy(%).
Methods
Storage Cost (MB) ↓
Compression Rate (%) ↑
ADD Woo and others (2022)
230.2
31.2
GAC-FAS Le and Woo (2024)
150.7
55.0
X-Pruner Yu and Xiang (2023)
133.9
60.0
One-bit Xu et al. (2024)
72.7
78.3
HOLa Lei et al. (2025)
86.6
74.1
DeFakeQ (Ours)
31.8
90.5
Table 5: Comparison with compression methods across other technical categories on Storage Cost (MB) and Compression Rate (%). ↓ means the lower the better and ↑ means the higher the better.
Method
CDF-v1 Li et al. (2020c)
CDF-v2 Li et al. (2020c)
DFD Zi et al. (2020)
DFDC Dolhansky et al. (2020)
DFDCP Dolhansky et al. (2019)
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
✗ HAQ, ✗ VEFT
57.8
58.9
53.2
46.6
48.2
48.0
52.2
54.9
51.9
42.3
45.8
43.4
52.5
57.1
55.8
✓ HAQ, ✗ VEFT
61.5
63.3
60.7
58.4
59.1
58.2
59.7
62.4
60.0
50.1
53.5
56.0
60.4
62.5
61.7
✗ HAQ, ✓ VEFT
65.2
61.9
66.6
64.5
60.8
68.2
63.6
64.4
62.4
55.9
57.8
60.1
57.2
59.8
60.2
✓ HAQ, ✓ VEFT (Ours)
73.5
76.4
76.3
69.3
74.7
73.7
74.5
78.1
77.1
64.1
67.2
65.0
69.2
72.3
71.9
Table 6: Ablation studies on model design (HAQ and VEFT). All detection models are trained on the FF++ dataset Rossler et al. (2019) and evaluated across various other datasets on metric detection accuracy(%).
Calibration Set Size
DFD Zi et al. (2020)
CDF-v1 Li et al. (2020c)
DFDC Dolhansky et al. (2020)
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
64
69.8
72.6
71.3
71.5
74.8
72.9
61.3
61.2
62.7
128
72.1
75.8
74.7
72.2
75.0
75.5
63.1
64.5
63.3
256
74.5
78.1
77.1
73.5
76.4
76.3
64.1
67.2
65.0
512
73.4
77.0
75.8
72.9
75.8
75.8
63.8
65.7
64.2
1024
73.1
76.8
75.5
72.6
75.5
75.6
63.5
65.3
64.0
Table 7: Ablations Studies on Calibration Set Size. All detection models are trained on the FF++ dataset Rossler et al. (2019) and evaluated across various other datasets on metric detection accuracy(%).
Method
CDF-v1 Li et al. (2020c)
CDF-v2 Li et al. (2020c)
DFD Zi et al. (2020)
DFDC Dolhansky et al. (2020)
DFDCP Dolhansky et al. (2019)
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
DeiT-Base
ViT-Base
Swin-Base
✗ VEFT
61.5
63.3
60.7
58.4
59.1
58.2
59.7
62.4
60.0
50.1
53.5
56.0
60.4
62.5
61.7
✓ VEFT (✗ Random Selection)
70.2
69.1
72.3
65.9
70.8
69.2
67.6
71.5
72.3
61.5
62.6
61.8
64.4
68.9
67.2
✓ VEFT (✓ Random Selection)
73.5
76.4
76.3
69.3
74.7
73.7
74.5
78.1
77.1
64.1
67.2
65.0
69.2
72.3
71.9
Table 8: Ablations Studies on Random Selection Strategy. All detection models are trained on the FF++ dataset Rossler et al. (2019) and evaluated across various other datasets on metric detection accuracy(%).
Figure 3: Visual Comparisons of detected salient forgeries during the quantization process.
Figure 4: Real-world detection instances of DeFakeQ deployed on an Android device. The quantized model processes in around 35 ms, and outputs the prediction result and the corresponding confidence score. Please refer to the demo video for our real time and real work depoyment on the android phone.
Deepfake detection models often rely on high-quality inputs, fixed inference paths, and computationally expensive architectures, limiting their use in low-resolution and resource-constrained settings. This paper proposes AdaGate-DF, an adaptive gated deepfake detection framework that uses image-quality cues to route samples through a dual multi-exit system so high-quality images can exit earlier and save compute. We evaluated AdaGate-DF against MaD-CoRN, DefakeHop++, and ShuffleNetV2 on two benchmark datasets (Celeb-DF and FaceForensics++) under multiple configurations to test image resolution dependence and training and inference efficiency. On Celeb-DF, AdaGate-DF achieves an AUC of 0.9370, outperforming MaD-CoRN and DefakeHop++ while maintaining a low inference latency. Resolution-based testing shows consistent improvement as input resolution increases, reaching an AUC of 0.9708 at 384 by 384. The FaceForensics++ results highlight that AdaGate-DF remains effective under class imbalance, following competitive results with evaluated models. Overall, AdaGate-DF demonstrated a practical balance between detection performance, uncertainty-aware prediction, and computational efficiency for variable-quality deepfake detection.
Vaishnavi Sen, Cody Laurie, Rashida Hasan
Department of Computer Science California State University, Northridge California, USA
Advances in speech synthesis have made deepfake speeches increasingly convincing, posing growing threats to security. While self-supervised learning (SSL) based detectors achieve state-of-the-art performance, their computational demands (typically 300M+ parameters) prevent deployment on resource-constrained devices. Existing compression methods, designed mainly for content-centric tasks, struggle to maintain competitive performance when directly adapted to deepfake detection. We propose a Task-Aware Joint Pruning and Distillation framework that combines cross-domain knowledge distillation with movement-guided structured pruning to transfer forgery-discriminative knowledge and preserve critical structures under aggressive compression. Our framework reduces the model to 31.9M parameters with 6.3× FLOPs reduction, with an average performance drop of only 1.30% across multiple datasets compared to the uncompressed baseline, demonstrating strong potential for on-device deployment.
Miao He, Peng Cheng, Zhongjie Ba +4
The State Key Laboratory of Blockchain and Data Security, Zhejiang University, China · Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, China · Qingdao Institute of Software College of Computer Science and Technology, China University of Petroleum (East China), China +1
Deepfake videos are increasingly challenging the credibility of online content. Many existing detection methodology relies on complex, resource-intensive models, which limit their practical use. The study introduces the ExpSpeech-Net deepfake detection (SqN-R-DFD) model, which utilizes SqueezeNet and RNN (Recurrent Neural Network) as its backbone, providing a lightweight and efficient deepfake detection framework that simultaneously analyzes facial expressions and speech patterns. The approach incorporates advanced feature extraction, such as ISLBT-based features for image and MPNCC for signals, along with a smart feature-selection strategy using SASMA (Sandpiper-Assisted Slime Mould Algorithm), ensuring optimal and balanced input to the detection models. By combining SqueezeNet and an RNN, subtle inconsistencies in deepfake videos are captured effectively. The framework achieves 94.5% accuracy, precision of 99.3%, and F-measure of 96.8%, outperforming conventional methods. This demonstrates that integrating multiple modalities with intelligent preprocessing and feature selection enables practical, real-time deepfake detection suitable for everyday applications.