Revisiting Simple Baselines for In-The-Wild Deepfake Detection

• URL: http://arxiv.org/abs/2509.04150v1
• Date: Thu, 04 Sep 2025 12:23:59 GMT
• Title: Revisiting Simple Baselines for In-The-Wild Deepfake Detection
• Abstract summary: We focus on the recently released "in-the-wild" benchmark, Deepfake-Eval-2024.<n>Three finetuned open-source models achieve accuracies between 61% and 69%, significantly lagging behind the leading commercial deepfake detector with 82% accuracy.<n>Our work revisits one of these baseline approaches, which adapts standard pretrained vision backbones to produce generalizable deepfake detectors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread adoption of synthetic media demands accessible deepfake detectors and realistic benchmarks. While most existing research evaluates deepfake detectors on highly controlled datasets, we focus on the recently released "in-the-wild" benchmark, Deepfake-Eval-2024. Initial reporting on Deepfake-Eval-2024 showed that three finetuned open-source models achieve accuracies between 61% and 69%, significantly lagging behind the leading commercial deepfake detector with 82% accuracy. Our work revisits one of these baseline approaches, originally introduced by Ojha et al., which adapts standard pretrained vision backbones to produce generalizable deepfake detectors. We demonstrate that with better-tuned hyperparameters, this simple approach actually yields much higher performance -- 81% accuracy on Deepfake-Eval-2024 -- surpassing the previously reported accuracy of this baseline approach by 18% and competing with commercial deepfake detectors. We discuss tradeoffs in accuracy, computational costs, and interpretability, focusing on how practical these deepfake detectors might be when deployed in real-world settings. Our code can be found at https://github.com/Deepfake-Detection-KKO/deepfake-detection.

Related papers

• Evaluating Deepfake Detectors in the Wild [0.0]
  We evaluate modern deepfake detectors, introducing a novel testing procedure designed to mimic real-world scenarios for deepfake detection.<n>Our analysis shows that detecting deepfakes still remains a challenging task.<n>Basic image manipulations, such as JPEG compression or image enhancement, can significantly reduce model performance.
  arXiv  Detail & Related papers  (2025-07-29T15:17:00Z)
• Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024 [6.865766644176186]
  We introduce Deepfake-Eval-2024, a new deepfake detection benchmark consisting of in-the-wild deepfakes collected from social media and deepfake detection platform users in 2024.<n>The benchmark contains diverse media content from 88 different websites in 52 different languages.<n>We find that the performance of open-source state-of-the-art deepfake detection models drops precipitously when evaluated on Deepfake-Eval-2024.
  arXiv  Detail & Related papers  (2025-03-04T18:33:22Z)
• Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook [101.30779332427217]
  We survey deepfake generation and detection techniques, including the most recent developments in the field.<n>We identify various kinds of deepfakes, according to the procedure used to alter or generate the fake content.<n>We develop a novel multimodal benchmark to evaluate deepfake detectors on out-of-distribution content.
  arXiv  Detail & Related papers  (2024-11-29T08:29:25Z)
• DF40: Toward Next-Generation Deepfake Detection [62.073997142001424]
  existing works identify top-notch detection algorithms and models by adhering to the common practice: training detectors on one specific dataset and testing them on other prevalent deepfake datasets. But can these stand-out "winners" be truly applied to tackle the myriad of realistic and diverse deepfakes lurking in the real world? We construct a highly diverse deepfake detection dataset called DF40, which comprises 40 distinct deepfake techniques.
  arXiv  Detail & Related papers  (2024-06-19T12:35:02Z)
• Facial Forgery-based Deepfake Detection using Fine-Grained Features [7.378937711027777]
  Facial forgery by deepfakes has caused major security risks and raised severe societal concerns. We formulate deepfake detection as a fine-grained classification problem and propose a new fine-grained solution to it. Our method is based on learning subtle and generalizable features by effectively suppressing background noise and learning discriminative features at various scales for deepfake detection.
  arXiv  Detail & Related papers  (2023-10-10T21:30:05Z)
• DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection [55.70982767084996]
  A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. We present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions. DeepfakeBench contains 15 state-of-the-art detection methods, 9CL datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations.
  arXiv  Detail & Related papers  (2023-07-04T01:34:41Z)
• Improving Fairness in Deepfake Detection [38.999205139257164]
  biases in the data used to train deepfake detectors can lead to disparities in detection accuracy across different races and genders. We propose novel loss functions that handle both the setting where demographic information is available as well as the case where this information is absent.
  arXiv  Detail & Related papers  (2023-06-29T02:19:49Z)
• DeepFake-Adapter: Dual-Level Adapter for DeepFake Detection [82.8662802404075]
  Existing deepfake detection methods fail to generalize well to unseen or degraded samples.<n>High-level semantics are indispensable recipes for generalizable forgery detection.<n>DeepFake-Adapter is first parameter-efficient tuning approach for deepfake detection.
  arXiv  Detail & Related papers  (2023-06-01T16:23:22Z)
• Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes [2.0883760606514934]
  We show that deepfake detectors proven to generalize well on multiple research datasets still struggle in real-world scenarios with well-crafted fakes. We propose a novel autoencoder for face swapping alongside an advanced face blending technique, which we utilize to generate 90 high-quality deepfakes.
  arXiv  Detail & Related papers  (2023-05-09T09:08:49Z)
• Voice-Face Homogeneity Tells Deepfake [56.334968246631725]
  Existing detection approaches contribute to exploring the specific artifacts in deepfake videos. We propose to perform the deepfake detection from an unexplored voice-face matching view. Our model obtains significantly improved performance as compared to other state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-03-04T09:08:50Z)
• WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection [82.42495493102805]
  We introduce a new dataset WildDeepfake which consists of 7,314 face sequences extracted from 707 deepfake videos collected completely from the internet. We conduct a systematic evaluation of a set of baseline detection networks on both existing and our WildDeepfake datasets, and show that WildDeepfake is indeed a more challenging dataset, where the detection performance can decrease drastically.
  arXiv  Detail & Related papers  (2021-01-05T11:10:32Z)

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.