Robust AI-Generated Face Detection with Imbalanced Data (2505.02182v1)

Abstract: Deepfakes, created using advanced AI techniques such as Variational Autoencoder and Generative Adversarial Networks, have evolved from research and entertainment applications into tools for malicious activities, posing significant threats to digital trust. Current deepfake detection techniques have evolved from CNN-based methods focused on local artifacts to more advanced approaches using vision transformers and multimodal models like CLIP, which capture global anomalies and improve cross-domain generalization. Despite recent progress, state-of-the-art deepfake detectors still face major challenges in handling distribution shifts from emerging generative models and addressing severe class imbalance between authentic and fake samples in deepfake datasets, which limits their robustness and detection accuracy. To address these challenges, we propose a framework that combines dynamic loss reweighting and ranking-based optimization, which achieves superior generalization and performance under imbalanced dataset conditions. The code is available at https://github.com/Purdue-M2/SP_CUP.

Robust AI-Generated Face Detection with Imbalanced Data: An Evaluation

In the contemporary digital landscape, the proliferation of AI-generated content, particularly deepfakes, poses substantial challenges to data authenticity and security. The paper "Robust AI-Generated Face Detection with Imbalanced Data" endeavors to address the limitations inherent in existing deepfake detection methodologies. The crux of the research is the development of a robust detection framework that effectively navigates the pitfalls of data imbalance and distribution shifts, which are prevalent issues in the field of deepfake detection.

Deepfake Detection Challenges and Methodologies

Deepfakes, synthesized via advanced AI techniques like Variational Autoencoders (VAE) and Generative Adversarial Networks (GANs), present considerable threats to digital trust. Historically, CNN-based techniques have been deployed to detect local artifacts in these fabricated media. However, the evolution of generative models necessitated a shift towards more sophisticated detectors employing Vision Transformers (ViTs) and multimodal models such as CLIP for global anomaly detection.

Despite these advances, two critical challenges persist:

1. Distribution Shifts: Emerging generative models can shift feature distributions, thereby reducing detector efficacy.
2. Class Imbalance: Authentic samples vastly outnumber deepfakes in datasets, impeding detector performance due to biases toward the majority class.

Proposed Framework

To mitigate these challenges, the paper proposes a novel framework that integrates dynamic loss reweighting with ranking-based optimization. This approach dynamically prioritizes learning from minority, fake samples, ensuring balanced class representation. The methodology utilizes a pre-trained CLIP model as a feature extractor followed by an MLP classifier. The framework augments feature space through noise-addition techniques, enhancing robustness against generative model advancements.

Learning Objectives

The framework's core learning objectives involve:

• Conditional Value at Risk (CVaR): This loss function focuses on hard-to-classify samples, improving detection robustness.
• Vector Scaling (VS) Loss with AUC Optimization: Aims at enhancing the model's discriminative capabilities to balance classification metrics and directly optimize AUC.

Moreover, optimization is reinforced through sharpness-aware minimization (SAM), which smooths the loss landscape to prevent overfitting.

Experimental Findings

Empirical evaluations conducted on the DFWild-Cup dataset exemplify the framework's superiority over traditional methods. Quantitative assessments highlighted improvements in key metrics such as AUC, accuracy, and F1-score, underscoring the framework's efficacy in navigating the imbalanced data constraints and providing robust classification even in adversarial perturbations.

Theoretical Implications and Future Prospects

The framework seamlessly integrates advanced AI techniques with pragmatic solutions to deepfake detection challenges. It underscores the necessity for adaptability in detector design, accommodating the ever-evolving landscape of generative models. The insights gleaned from this research not only show promise for real-world applications but also lay the groundwork for future explorations into AI-driven content authentication.

The next avenues for exploration include refining augmentation strategies and enhancing detector robustness against novel generative models. Future research could explore additional modalities, such as AI-generated voice detection, broadening the scope of content integrity assurance.

In conclusion, the paper presents a comprehensive exploration into tackling deepfake detection with imbalanced data, establishing a robust foundation for mitigating threats posed by AI-generated content. Through focused methodology and empirical validation, it contributes significantly to advancing reliable, adaptable detection technologies in safeguarding digital media authenticity.