Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection

• URL: http://arxiv.org/abs/2405.15173v2
• Date: Wed, 08 Oct 2025 19:27:12 GMT
• Title: Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection
• Authors: Xinan He, Yue Zhou, Shu Hu, Bin Li, Jiwu Huang, Feng Ding,
• Abstract summary: Deepfake technology is essential for safeguarding trust in digital communication and protecting individuals.<n>Current detectors often suffer from a dual-overfitting: they become overly specialized in both specific fingerprints and particular demographic attributes.<n>We propose a novel strategy called misleading-learning, which populates the latent space with a multitude of redundant environments.
• Score: 41.53648814855822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting falsified faces generated by Deepfake technology is essential for safeguarding trust in digital communication and protecting individuals. However, current detectors often suffer from a dual-overfitting: they become overly specialized in both specific forgery fingerprints and particular demographic attributes. Critically, most existing methods overlook the latter issue, which results in poor fairness: faces from certain demographic groups, such as different genders or ethnicities, are consequently more difficult to reliably detect. To address this challenge, we propose a novel strategy called misleading-learning, which populates the latent space with a multitude of redundant environments. By exposing the detector to a sufficiently rich and balanced variety of high-level information for demographic fairness, our approach mitigates demographic bias while maintaining a high detection performance level. We conduct extensive evaluations on fairness, intra-domain detection, cross-domain generalization, and robustness. Experimental results demonstrate that our framework achieves superior fairness and generalization compared to state-of-the-art approaches.

Related papers

• Fairness-Aware Deepfake Detection: Leveraging Dual-Mechanism Optimization [13.52582347670271]
  Biases in detection models toward different demographic groups, such as gender and race, may lead to systemic misjudgments.<n>We propose a dual-mechanism collaborative optimization framework to address this challenge.<n>Our framework improves both inter-group and intra-group fairness while maintaining overall detection accuracy across domains.
  arXiv  Detail & Related papers  (2025-11-13T10:04:45Z)
• Rethinking Occlusion in FER: A Semantic-Aware Perspective and Go Beyond [10.015531203047598]
  We present ORSANet, which introduces auxiliary multi-modal semantic guidance to disambiguate facial occlusion.<n>We also introduce facial landmarks as sparse geometric prior to mitigate intrinsic noises in FER, such as identity and gender biases.<n>Our proposed ORSANet achieves SOTA recognition performance.
  arXiv  Detail & Related papers  (2025-07-21T09:04:29Z)
• Rethinking Individual Fairness in Deepfake Detection [7.926090411049054]
  Generative AI models have substantially improved the realism of synthetic media, yet their misuse through sophisticated DeepFakes poses significant risks.<n>Despite recent advances in deepfake detection, fairness remains inadequately addressed, enabling deepfake markers to exploit biases against specific populations.<n>We propose the first generalizable framework that can be integrated into existing deepfake detectors to enhance individual fairness and generalization.
  arXiv  Detail & Related papers  (2025-07-18T19:04:47Z)
• Ambiguity-aware Point Cloud Segmentation by Adaptive Margin Contrastive Learning [65.94127546086156]
  We propose an adaptive margin contrastive learning method for semantic segmentation on point clouds.<n>We first design AMContrast3D, a method comprising contrastive learning into an ambiguity estimation framework.<n>Inspired by the insight of joint training, we propose AMContrast3D++ integrating with two branches trained in parallel.
  arXiv  Detail & Related papers  (2025-07-09T07:00:32Z)
• Fair Deepfake Detectors Can Generalize [51.21167546843708]
  We show that controlling for confounders (data distribution and model capacity) enables improved generalization via fairness interventions.<n>Motivated by this insight, we propose Demographic Attribute-insensitive Intervention Detection (DAID), a plug-and-play framework composed of: i) Demographic-aware data rebalancing, which employs inverse-propensity weighting and subgroup-wise feature normalization to neutralize distributional biases; and ii) Demographic-agnostic feature aggregation, which uses a novel alignment loss to suppress sensitive-attribute signals.<n>DAID consistently achieves superior performance in both fairness and generalization compared to several state-of-the-art
  arXiv  Detail & Related papers  (2025-07-03T14:10:02Z)
• Trident: Detecting Face Forgeries with Adversarial Triplet Learning [5.101710685818588]
  textitTrident is trained on curated triplets to isolate nuanced differences of forgeries.<n> domain-adversarial training with a forgery discriminator guides our embedding model towards forgery-agnostic representations.
  arXiv  Detail & Related papers  (2025-06-29T11:17:25Z)
• Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective [54.605073936695575]
  Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection.<n>Existing methods rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality.<n>The presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process.<n>We propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process.
  arXiv  Detail & Related papers  (2025-05-23T15:05:56Z)
• Cross-Branch Orthogonality for Improved Generalization in Face Deepfake Detection [43.2796409299818]
  Deepfakes are becoming a nuisance to law enforcement authorities and the general public.<n>Existing deepfake detectors are struggling to keep up with the pace of improvements in deepfake generation.<n>This paper proposes a new strategy that leverages coarse-to-fine spatial information, semantic information, and their interactions.
  arXiv  Detail & Related papers  (2025-05-08T01:49:53Z)
• From Visual Explanations to Counterfactual Explanations with Latent Diffusion [11.433402357922414]
  We propose a new approach to tackle two key challenges in recent prominent works. First, we determine which specific counterfactual features are crucial for distinguishing the "concept" of the target class from the original class. Second, we provide valuable explanations for the non-robust classifier without relying on the support of an adversarially robust model.
  arXiv  Detail & Related papers  (2025-04-12T13:04:00Z)
• A Knowledge-guided Adversarial Defense for Resisting Malicious Visual Manipulation [93.28532038721816]
  Malicious applications of visual manipulation have raised serious threats to the security and reputation of users in many fields.<n>We propose a knowledge-guided adversarial defense (KGAD) to actively force malicious manipulation models to output semantically confusing samples.
  arXiv  Detail & Related papers  (2025-04-11T10:18:13Z)
• Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective [100.54185280153753]
  We find that both classifier guidance and classifier-free guidance achieve conditional generation by pushing the denoising diffusion trajectories away from decision boundaries. We propose a generic postprocessing step built upon flow-matching to shrink the gap between the learned distribution for a pretrained denoising diffusion model and the real data distribution.
  arXiv  Detail & Related papers  (2025-03-13T17:59:59Z)
• Generalized Interpolating Discrete Diffusion [65.74168524007484]
  Masked diffusion is a popular choice due to its simplicity and effectiveness. We derive the theoretical backbone of a family of general interpolating discrete diffusion processes. Exploiting GIDD's flexibility, we explore a hybrid approach combining masking and uniform noise.
  arXiv  Detail & Related papers  (2025-03-06T14:30:55Z)
• One-for-More: Continual Diffusion Model for Anomaly Detection [61.12622458367425]
  Anomaly detection methods utilize diffusion models to generate or reconstruct normal samples when given arbitrary anomaly images. Our study found that the diffusion model suffers from severe faithfulness hallucination'' and catastrophic forgetting'' We propose a continual diffusion model that uses gradient projection to achieve stable continual learning.
  arXiv  Detail & Related papers  (2025-02-27T07:47:27Z)
• Effort: Efficient Orthogonal Modeling for Generalizable AI-Generated Image Detection [66.16595174895802]
  Existing AI-generated image (AIGI) detection methods often suffer from limited generalization performance. In this paper, we identify a crucial yet previously overlooked asymmetry phenomenon in AIGI detection.
  arXiv  Detail & Related papers  (2024-11-23T19:10:32Z)
• Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI [59.96044730204345]
  We introduce Derivative-Free Diffusion Manifold-Constrainted Gradients (FreeMCG) FreeMCG serves as an improved basis for explainability of a given neural network. We show that our method yields state-of-the-art results while preserving the essential properties expected of XAI tools.
  arXiv  Detail & Related papers  (2024-11-22T11:15:14Z)
• Rethinking Weak-to-Strong Augmentation in Source-Free Domain Adaptive Object Detection [38.596886094105216]
  Source-Free domain adaptive Object Detection (SFOD) aims to transfer a detector (pre-trained on source domain) to new unlabelled target domains. This paper introduces a novel Weak-to-Strong Contrastive Learning (WSCoL) approach.
  arXiv  Detail & Related papers  (2024-10-07T23:32:06Z)
• Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information [75.36597470578724]
  Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. We propose gUided Purification (COUP) algorithm, which purifies while keeping away from the classifier decision boundary. Experimental results show that COUP can achieve better adversarial robustness under strong attack methods.
  arXiv  Detail & Related papers  (2024-08-12T02:48:00Z)
• Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations [63.52709761339949]
  We first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods.<n>We design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results.<n>We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates.
  arXiv  Detail & Related papers  (2024-07-19T14:53:18Z)
• Decoupling Forgery Semantics for Generalizable Deepfake Detection [6.1822981823804835]
  We propose a novel method for detecting DeepFakes, enhancing the generalization of detection through semantic decoupling. Evaluation on FF++, Celeb-DF, DFD, and DFDC datasets showcases our method's excellent detection and generalization performance.
  arXiv  Detail & Related papers  (2024-06-14T06:00:14Z)
• Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Models [55.99654128127689]
  Visual Foundation Models (VFMs) are used to generate semantic labels for weakly-supervised pixel-to-point contrastive distillation. We adapt sampling probabilities of points to address imbalances in spatial distribution and category frequency. Our approach consistently surpasses existing image-to-LiDAR contrastive distillation methods in downstream tasks.
  arXiv  Detail & Related papers  (2024-05-23T07:48:19Z)
• Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection [96.539862328788]
  Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. We propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF) Experiment results on several public face forgery datasets demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-21T08:58:49Z)
• Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition [36.3636416735057]
  We present a semantic contrastive bootstrapping (Scob) approach to gradually recover the cross-object relationships. We then propose a recurrent semantic masked transformer to extract iconic object-level representations. Extensive experimental results demonstrate that the proposed joint learning framework surpasses the state-of-the-art models.
  arXiv  Detail & Related papers  (2023-07-15T01:59:53Z)
• STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection [80.04000067312428]
  We propose a Self-adapTive Ambiguity Reduction (STAR) loss by exploiting the properties of semantic ambiguity. We find that semantic ambiguity results in the anisotropic predicted distribution, which inspires us to use predicted distribution to represent semantic ambiguity. We also propose two kinds of eigenvalue restriction methods that could avoid both distribution's abnormal change and the model's premature convergence.
  arXiv  Detail & Related papers  (2023-06-05T10:33:25Z)
• Scalable Bayesian Meta-Learning through Generalized Implicit Gradients [64.21628447579772]
  Implicit Bayesian meta-learning (iBaML) method broadens the scope of learnable priors, but also quantifies the associated uncertainty. Analytical error bounds are established to demonstrate the precision and efficiency of the generalized implicit gradient over the explicit one.
  arXiv  Detail & Related papers  (2023-03-31T02:10:30Z)
• Diffusion Models for Adversarial Purification [69.1882221038846]
  Adrial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. We propose DiffPure that uses diffusion models for adversarial purification. Our method achieves the state-of-the-art results, outperforming current adversarial training and adversarial purification methods.
  arXiv  Detail & Related papers  (2022-05-16T06:03:00Z)
• Real-centric Consistency Learning for Deepfake Detection [8.313889744011933]
  We tackle the deepfake detection problem through learning the invariant representations of both classes. We propose a novel forgery semantical-based pairing strategy to mine latent generation-related features. At the feature level, based on the centers of natural faces at the representation space, we design a hard positive mining and synthesizing method to simulate the potential marginal features.
  arXiv  Detail & Related papers  (2022-05-15T07:01:28Z)
• Few-shot Forgery Detection via Guided Adversarial Interpolation [56.59499187594308]
  Existing forgery detection methods suffer from significant performance drops when applied to unseen novel forgery approaches. We propose Guided Adversarial Interpolation (GAI) to overcome the few-shot forgery detection problem. Our method is validated to be robust to choices of majority and minority forgery approaches.
  arXiv  Detail & Related papers  (2022-04-12T16:05:10Z)
• Fairness without the sensitive attribute via Causal Variational Autoencoder [17.675997789073907]
  Due to privacy purposes and var-ious regulations such as RGPD in EU, many personal sensitive attributes are frequently not collected. By leveraging recent developments for approximate inference, we propose an approach to fill this gap. Based on a causal graph, we rely on a new variational auto-encoding based framework named SRCVAE to infer a sensitive information proxy.
  arXiv  Detail & Related papers  (2021-09-10T17:12:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.