Generalizable Face Forgery Detection via Separable Prompt Learning

• URL: http://arxiv.org/abs/2604.17307v1
• Date: Sun, 19 Apr 2026 07:51:34 GMT
• Title: Generalizable Face Forgery Detection via Separable Prompt Learning
• Abstract summary: We propose a new Separable Prompt Learning strategy (SePL) that enables CLIP to serve as an effective face forgery detector.<n>To achieve this disentangle, we describe a cross-modality alignment strategy and a set of dedicated objectives.<n>Our method achieves competitive and even superior performance compared to other methods under both cross-dataset and cross-method evaluation.
• Score: 9.467877750964588
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting face forgeries using CLIP has recently emerged as a promising and increasingly popular research direction. Owing to its rich visual knowledge acquired through large-scale pretraining, most existing methods typically rely on the visual encoder of CLIP, while paying limited attention to the text modality. Given the instructive nature of the text modality, we posit that it can be leveraged to instruct Deepfake detection with meticulous design. Accordingly, we shift the focus from the visual modality to the text modality and propose a new Separable Prompt Learning strategy (SePL) that enables CLIP to serve as an effective face forgery detector. The core idea of SePL is to disentangle forgery-specific and forgery-irrelevant information in images via two types of prompt learning, with the former enhancing detection. To achieve this disentangle, we describe a cross-modality alignment strategy and a set of dedicated objectives. Extensive experiments demonstrate that, with this simple adaptation, our method achieves competitive and even superior performance compared to other methods under both cross-dataset and cross-method evaluation, highlighting its strong generalizability. The codes have been released at https://github.com/OUC-YER/SePL-DeepfakeDetection

Related papers

• SAPL: Semantic-Agnostic Prompt Learning in CLIP for Weakly Supervised Image Manipulation Localization [45.19935082419337]
  Malicious image manipulation threatens public safety and requires efficient localization methods.<n>Existing weakly supervised methods rely on image-level binary labels and focus on global classification.<n>We propose Semantic-Agnostic Prompt Learning (SAPL) in CLIP, which learns text prompts that intentionally encode non-semantic, boundary-centric cues.
  arXiv  Detail & Related papers  (2026-01-09T07:25:55Z)
• Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detection [71.60120616284246]
  We propose a novel knowledge-based prompt learning framework to explore the strong generalization capability of vision-language models for 3D mask presentation attack detection.<n> Experimental results demonstrate that the proposed method achieves state-of-the-art intra- and cross-scenario detection performance.
  arXiv  Detail & Related papers  (2025-05-06T15:09:23Z)
• Unlocking the Hidden Potential of CLIP in Generalizable Deepfake Detection [23.48106270102081]
  This paper tackles the challenge of detecting partially manipulated facial deepfakes.<n>We leverage the Contrastive Language-Image Pre-training (CLIP) model, specifically its ViT-L/14 visual encoder.<n>The proposed approach utilizes parameter-efficient fine-tuning (PEFT) techniques, such as LN-tuning, to adjust a small subset of the model's parameters.
  arXiv  Detail & Related papers  (2025-03-25T14:10:54Z)
• C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection [98.34703790782254]
  We introduce Category Common Prompt CLIP, which integrates the category common prompt into the text encoder to inject category-related concepts into the image encoder.<n>Our method achieves a 12.41% improvement in detection accuracy compared to the original CLIP, without introducing additional parameters during testing.
  arXiv  Detail & Related papers  (2024-08-19T02:14:25Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model [55.321010757641524]
  We introduce CLIP4STR, a simple yet effective STR method built upon image and text encoders of CLIP.<n>We scale CLIP4STR in terms of the model size, pre-training data, and training data, achieving state-of-the-art performance on 13 STR benchmarks.
  arXiv  Detail & Related papers  (2023-05-23T12:51:20Z)
• HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models [30.279621764192843]
  Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors. We propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization.
  arXiv  Detail & Related papers  (2023-03-28T07:54:54Z)
• Three ways to improve feature alignment for open vocabulary detection [88.65076922242184]
  Key problem in zero-shot open vocabulary detection is how to align visual and text features, so that the detector performs well on unseen classes. Previous approaches train the feature pyramid and detection head from scratch, which breaks the vision-text feature alignment established during pretraining. We propose three methods to alleviate these issues. Firstly, a simple scheme is used to augment the text embeddings which prevents overfitting to a small number of classes seen during training. Secondly, the feature pyramid network and the detection head are modified to include trainable shortcuts. Finally, a self-training approach is used to leverage a larger corpus of
  arXiv  Detail & Related papers  (2023-03-23T17:59:53Z)
• Turning a CLIP Model into a Scene Text Detector [56.86413150091367]
  Recently, pretraining approaches based on vision language models have made effective progresses in the field of text detection. This paper proposes a new method, termed TCM, focusing on Turning the CLIP Model directly for text detection without pretraining process.
  arXiv  Detail & Related papers  (2023-02-28T06:06:12Z)

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.