Few-Shot Learner Generalizes Across AI-Generated Image Detection

Dear Reviewer fx2j, Thank you for your feedback and constructive comments. We appreciate the time and effort you invested in reviewing our work. Here are our responses to your concerns:

Q1. Academic contribution is quite limited.

To the best of our knowledge, our work is the first to systematically adapt few-shot learning for AIGC detection. While prior research has primarily focused on improving model generalization, our work uniquely leverages few-shot learning to mitigate performance degradation when detecting synthetic images from unseen domains or new generative models. The ability to achieve superior generalization with minimal samples from emerging generative models represents a key innovation of our approach. Specifically, our work makes the following key contributions:

1. Novel Adaptation of Few-Shot Learning: We propose a pioneering framework that leverages few-shot learning to significantly reduce performance degradation when detecting synthetic images from unseen domains or new generative models. This is particularly impactful given the rapid evolution of generative AI technologies.
2. Practical Efficiency: Unlike existing methods that require extensive retraining on new collected data, our approach achieves robust generalization with only minimal samples from new domains, offering a more scalable and resource-efficient solution.
3. Demonstration and Insights: Through comprehensive experiments, we demonstrate that our few-shot adaptation outperforms traditional fine-tuning approaches on widely used benchmarks.

Furthermore, AIGC detection is a question-driven task, and our work provides a novel solution to address it. Our work introduces a new perspective in this field which can inspire future research directions and pave the way for more adaptive detection frameworks in the challenge of rapidly evolving generative technologies.

Q2. ProtoNet is outdated in the few-shot learning domain.

Our primary objective is to validate the fundamental effectiveness of few-shot learning. For this purpose, we intentionally adopt this vanilla yet classic method to ensure a comprehensive and interpretable evaluation. ProtoNet remains a principled and widely recognized baseline in few-shot learning, which allows us to isolate and rigorously assess the performance of few-shot learning without the confounding factors introduced by more complex tricks. Therefore, ProtoNet serves as an ideal choice due to its conceptual simplicity and proven reliability. This approach can help us better analyze the intrinsic advantages of few-shot learning, offering insights that may be obscured by more sophisticated but less concise methods.

Q3. This paper does not provide a comparison with the given approach [1].

We appreciate your insightful comment regarding the comparison with the approach presented in [1]. Our paper focuses on evaluating few-shot learning methods for AI-generated image detection, and we selected several widely recognized baselines for comparison, which have also been used by this work [2]. We will include comparisons with CLIP-based methods for a more comprehensive evaluation.

Q4. Lack testing on large-scale/high-quality datasets.

As emphasized in our paper, the primary goal of this work is to propose a novel few-shot learning framework for AI-generated image detection. We adopt the GenImage benchmark due to its widespread use in prior research, which facilitates direct comparisons with existing methods. While numerous datasets have been proposed for deepfake detection, few have gained broad recognition as foundational benchmarks. This study [2] presented at ICLR 2025 is too recent to be thoroughly validated in the literature. Additionally, our current evaluation is constrained by extremely limited computational resources. We appreciate this insightful suggestion and plan to include more experimental results in the supplementary materials.

In conclusion, our endeavor is to introduce a novel few-shot learning application for AIGC detection, addressing the critical gap in generalization for rapidly evolving generative models. We sincerely appreciate your constructive feedback, which is helpful for us to identify key areas for further refinement. While we acknowledge the current limitations, we believe our study offers a new perspective in AIGC detection, and its methodological innovation—coupled with empirical validation—merits reconsideration for publication. We would sincerely appreciate it if you would reconsider the potential and novelty of our contribution. Thank you again for your time and insightful critique.

[1] Sohail Ahmed Khan and Duc-Tien Dang-Nguyen. 2024. CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection. In Proceedings of the 2024 International Conference on Multimedia Retrieval. Association for Computing Machinery, New York, NY, USA, 1006–1015.

[2] Yan et al. A Sanity Check for AI-generated Image Detection. CoRR, abs/2406.19435, 2024.

Dear Reviewer N8UV, Thank you for your detailed feedback and constructive comments. We have carefully considered each point you raised and would like to address them as follows:

Q1: The lack of strategy for obtaining test images from the same domain in real-world applications.

While current diffusion models are resource-intensive to train, widely adopted models are often accessible via APIs or open-source releases, serving as representative models. Our approach requires only a small batch of images from such a model to effectively detect generated content—not only from the same model but also across similar domains. Additionally, we have proposed a zero-shot detecting method in Section 3.3 to assess samples outside the training domain, ensuring robust generalization in real-world scenarios.

Q2: Lack of a new benchmark for multiple domain generalization.

Current widely used large-scale synthetic image datasets like GenImage and ForenSynths [1] have provided valuable resources for fake image detection, containing samples from diverse generative models with clear source annotations. We identify a critical gap in current research that most existing methods focus narrowly on binary classification (real vs. fake), overlooking the substantial domain-specific characteristics across different generative models. Our work serves as the first framework for multi-domain detection, and we have transformed GenImage dataset to a suitable benchmark for this task, laying groundwork for future benchmark creation once dataset diversity matures.

Q3: A fake image may share similar content with real images.

Our approach is based on the observation that synthetic images generated by different AI models exhibit distinct artifacts, as demonstrated in prior research [2]. While existing solutions often rely on training separate binary classifiers for distinct generative models—a process that is computationally expensive and impractical for large-scale deployment—our method takes a more efficient and generalizable approach. By focusing on classifying images based on their source generators rather than relying solely on content-based features, our network is explicitly trained to identify these model-specific artifacts. This design of our model ensures that it prioritizes forensic traces (e.g., noise patterns, texture inconsistencies, or spectral discrepancies) over semantic content, which is a key limitation in CLIP-based and other content-driven detection systems.

Q4: This approach does not align with the standard practices of single domain generalization.

Thank you for raising this important point. You're absolutely right to point out the divergence from conventional single domain generalization approaches. As detailed in our paper Section 4.2, this difference stems from our unique training paradigm which fundamentally requires diverse class samples during the training phase. We acknowledge that this represents a departure from standard practices, but it's a deliberate design choice that enables our model to learn more transferable features across domains. We anticipate that expanding the class diversity during training would further enhance the model's generalization capability, as it would allow the model to learn even more robust feature representations. However, this strategy may not generalize well to binary-classification tasks, where artifacts across categories can exhibit significant differences and shared characteristics among synthetic images may be absent. This is an important direction we plan to explore in future work. We appreciate this thoughtful observation and will carefully address this methodological distinction in our final version to ensure proper contextualization within the field of domain generalization research.

Our approach pioneers a novel few-shot learning framework for deepfake detection and systematically explores the potential of multi-class detection in this field, demonstrating the feasibility of few-shot forensic analysis across generative models. While there exist limitations, it provides foundational insights for future research to address evolving synthetic threats. We sincerely appreciate your expertise in guiding methodological refinements, and we hope these contributions could be considered in the final assessment. Thank you for your time and understanding.

[1] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. 2020. CNN-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 8695–8704

[2] Liu, Fengyuan , et al. "Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution." European Conference on Computer Vision Springer, Cham, 2025.

Dear Reviewer aJMC, Many thanks for your careful reading and valuable comments. We hope our reply further reduces potential misunderstandings.

Q1. How does FSD perform when faced with generative models that are significantly different from those in the training set?

Our comprehensive evaluation on the GenImage dataset has examined FSD's cross-generator generalization capability across diverse diffusion models with varying architectures, covering both pixel space and latent space image generation. The results provide valuable insights into the detector's robustness against structurally different generative models. While these initial findings demonstrate promising generalization performance, we acknowledge the need for further validation with real-world generative models and plan to expand our testing in future work to strengthen these conclusions.

Q2. Is there a way to improve performance without relying on samples from unseen classes in zero-shot scenarios?

We acknowledge the performance gap in zero-shot settings and agree that integrating domain adaptation or meta-learning could further improve generalization. In our work, we have observed that generative models with similar structures tend to cluster closer together in the learned metric space. This suggests that as we accumulate sufficient samples from representative model types, detecting images from fine-tuned or LoRA-trained models should become easier and more straightforward. We anticipate this collection of representative samples will help minimize the current performance gap in zero-shot detection.

Q3: How sensitive is the method to the choice of support samples?

Our observations indicate that with fewer than 5 samples, the results tend to fluctuate within a certain range, showing noticeable variability. However, as the number of samples increases to around 100 (which is typically not difficult to collect in practice), the performance becomes significantly more stable and reliable. This suggests that the method achieves robust and confident outcomes when provided with an acceptable number of support samples.

In conclusion, our approach pioneers a few-shot-learning way for deepfake detection. We believe this direction holds potential as a future trend in the domain. We will revise the manuscript to include more details about the zero-shot classification and training strategy. Thank you for your consideration.