Quantum-Inspired Fine-Tuning for Few-Shot AIGC Detection via Phase-Structured Reparameterization
Authors: Kaiyang Xing, Han Fang, Zhaoyun Chen, Zhonghui Li, Yang Yang, Weiming Zhang, Guoping Guo
Organizations: Anhui University, Hefei, China · National University of Singapore, Singapore, Singapore · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China · University of Science and Technology of China, Hefei, China
Recent studies show that quantum neural networks (QNNs) generalize well in few-shot regimes. To extend this advantage to large-scale tasks, we propose Q-LoRA, a quantum-enhanced fine-tuning scheme that integrates lightweight QNNs into the low-rank adaptation (LoRA) adapter. Applied to AI-generated content (AIGC) detection, Q-LoRA consistently outperforms standard LoRA under few-shot settings. We analyze the source of this improvement and identify two possible structural inductive biases from QNNs: (i) phase-aware representations, which encode richer information across orthogonal amplitude-phase components, and (ii) norm-constrained transformations, which stabilize optimization via inherent orthogonality. However, Q-LoRA incurs non-trivial overhead due to quantum simulation. Motivated by our analysis, we further introduce H-LoRA, a fully classical variant that applies the Hilbert transform within the LoRA adapter to retain similar phase structure and constraints. Experiments on few-shot AIGC detection show that both Q-LoRA and H-LoRA outperform standard LoRA by over 5% accuracy, with H-LoRA achieving comparable accuracy at significantly lower cost in this task.
Figures & tables
Figure 1: Method of LoRA and Q-LoRA applied to the CLIP model, and their detection accuracy for AI-generated image detection tasks under different training samples.
Model SAMPLES
Metrics
CLIP
LoRA
Q-LoRA
H-LoRA
200
400
800
200
400
800
200
400
800
200
400
800
Midjourney
AUC(%)
98.38
98.29
98.38
94.02
95.78
97.59
98.62
98.74
98.68
96.31
97.80
97.91
ACC(%)
93.40
92.85
91.85
82.60
80.55
78.70
90.40
86.15
81.75
86.10
81.90
84.85
PR(%)
93.86
90.96
88.56
77.80
73.42
71.37
87.62
79.93
73.45
80.37
75.08
77.95
RE(%)
93.00
95.50
96.50
93.60
97.40
98.20
96.00
98.60
99.50
96.40
98.40
98.70
Wukong
AUC(%)
97.31
99.00
99.27
97.22
99.34
99.85
99.43
99.83
99.97
99.18
99.84
99.94
Table 1: Performance comparison under different few-shot settings for AI-generated image detection. For each metric, the best and second-best results within the same training sample size are highlighted in bold and underline, respectively.
Figure 3: Q-LoRA and H-LoRA t-SNE visualization
Figure 4: Attention visualization of Q-LoRA and H-LoRA
Method
Samples
AUC(%)
F1(%)
ACC(%)
EER
50
90.85
72.54
72.99
0.1616
Whisper
100
93.71
87.42
88.19
0.1260
200
95.82
89.93
90.69
0.0957
50
92.86
78.89
80.34
0.1358
LoRA
100
97.45
89.39
90.40
0.0711
200
99.04
95.54
95.67
0.0431
Table 2: Algorithm performance with varying sample sizes in audio forgery detection
Method
H-LoRA
Q-LoRA
Inference Time(sec)
0.09
65.68
Training Time(epoch/sec)
4.07
2088.34
Additional Parameter
0
24
Table 3: Efficiency comparison between H-LoRA and Q-LoRA.
Figure 5: Accuracy of LoRA, Q-LoRA and H-LoRA under different ranks and training sample sizes
Method
H-LoRA
Linear
Tanh
Sigmoid
Silu
AUC(%)
97.12
96.32
96.51
96.89
97.01
ACC(%)
89.94
87.79
88.30
87.19
88.36
Pr(%)
87.03
85.57
85.70
84.20
85.91
Re(%)
96.40
96.44
95.14
95.87
95.59
Table 4: Performance of different types of intermediate layers in LoRA
Method
H-LoRA
1
2
3
4
5
AUC(%)
97.12
96.32
97.39
97.52
97.58
97.61
ACC(%)
89.94
87.79
88.90
89.60
89.10
89.30
Pr(%)
87.03
85.57
86.04
86.25
85.77
86.68
Re(%)
96.40
96.44
96.41
97.19
97.17
96.23
Table 5: Performance of Number of Intermediate Linear Layers in LoRA
Faculty of Computing Universiti Teknologi Malaysia Johor Bahru, Malaysia · Remote Student based in Jakarta, Indonesia · Informatics Department Faculty of Science and Technology Universitas Muhammadiyah Sidoarjo Sidoarjo, Indonesia