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arxiv: 2502.19716 · v2 · submitted 2025-02-27 · 💻 cs.CV · cs.LG

Fully AI-Generated Image Detection: Definition, Recent Advances and Challenges

Qijie Xu , Can Wang , Jiawei Chen , Siwei Lyu , Defang Chen This is my paper

Pith reviewed 2026-05-23 03:08 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords fully AI-generated image detectiondeepfake detectionartifact extractioninductive priorsdataset constructionimage forensicsgenerative modelsAI media forensics
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The pith

Fully AI-generated image detectors are organized by the inductive priors they use to extract generation artifacts, with dataset choices controlling generalization.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This review organizes the problem of spotting images made entirely by AI models into dataset construction and artifact extraction. It groups extraction methods according to the inductive priors each one exploits to find traces left by generative architectures. The structure shows how training data choices shape whether those traces remain reliable on new models. Readers would value this because advancing generators make deepfakes harder to catch, and a clear map of existing approaches highlights where detection still breaks down. The paper closes by naming open problems and possible next steps for stronger detectors.

Core claim

The paper claims that detecting fully AI-generated images requires reliably extracting the inherent artifacts imprinted by generative architectures, and that existing methods can be categorized based on the primary inductive priors leveraged to isolate artifacts while dataset design influences generalization and robustness. It follows the standard detector design pipeline to survey works and identify open challenges.

What carries the argument

The categorization framework that groups artifact extraction methods by the primary inductive priors they leverage to isolate artifacts imprinted by generative architectures.

If this is right

  • Dataset construction choices directly determine how well learned artifacts transfer to unseen generative models.
  • Methods are reviewed and compared within a shared structure of inductive priors rather than isolated techniques.
  • Gaps in current artifact extraction approaches become visible once all works are placed in the same framework.
  • Future detectors can be designed by deliberately selecting priors and datasets that address identified robustness shortfalls.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same inductive-prior lens could be applied to detection tasks in video or audio to reveal cross-media patterns.
  • Hybrid detectors that combine multiple priors might achieve better robustness than single-prior methods.
  • Standardized evaluation protocols built around this categorization could make progress across papers easier to compare.

Load-bearing premise

The body of existing literature on fully AI-generated image detection can be systematically and comprehensively categorized using the proposed framework of inductive priors for artifact extraction.

What would settle it

A detection method whose core mechanism cannot be assigned to any of the inductive-prior categories defined in the review, or empirical tests showing dataset design has little measurable effect on generalization performance.

Figures

Figures reproduced from arXiv: 2502.19716 by Can Wang, Defang Chen, Jiawei Chen, Qijie Xu, Siwei Lyu.

Figure 1
Figure 1. Figure 1: Generated images can now easily mislead the public, leading to serious consequences such as panic and economic losses. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A taxonomy of recent diffusion-generated image detection methods. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
read the original abstract

Recent advances in visual generative models have enabled the creation of highly realistic, fully AI-generated images without relying on real source content. While beneficial for many applications, these models also pose significant societal risks, as they can be easily exploited to produce convincing Deepfakes. Detecting them represents a foundational yet challenging problem in AI media forensics, requiring detectors to reliably extract the inherent artifacts imprinted by generative architectures. In this Review, we provide a systematic overview of fully AI-generated image detection. Following the standard detector design pipeline, we focus on two key components: dataset construction and artifact extraction. We analyze how dataset design influences the generalization and robustness of learned artifacts, and categorize existing artifact extraction methods based on the primary inductive priors leveraged to isolate artifacts. Within this framework, we systematically review existing works. Finally, we highlight open problems and envision several future directions for developing more robust and generalizable detectors. Reviewed works in this survey can be found at https://github.com/zju-pi/Awesome-Fully-AI-Generated-Image-Detection.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript is a survey on fully AI-generated image detection. It follows the standard detector design pipeline and focuses on two components: dataset construction and artifact extraction. The authors claim that dataset design choices influence the generalization and robustness of learned artifacts, and they categorize existing artifact extraction methods according to the primary inductive priors used to isolate generative artifacts. Within this framework the paper systematically reviews prior works, identifies open problems, and outlines future directions. A GitHub repository listing the reviewed works is provided.

Significance. If the proposed organizational framework holds, the survey supplies a useful lens for navigating the rapidly growing literature on AI media forensics by linking inductive priors to artifact detection and by explicitly connecting dataset construction decisions to detector robustness. The inclusion of a public GitHub repository that enumerates the reviewed works is a concrete strength that supports reproducibility and follow-on research. As a review paper the contribution lies in synthesis and structuring rather than new empirical results.

minor comments (3)
  1. [Abstract] Abstract: the GitHub link is mentioned but the introduction does not describe its contents or update policy, reducing immediate utility for readers.
  2. [§3 (Artifact Extraction)] The categorization of inductive priors would be clearer if a summary table were added that lists each prior, representative papers, and the key artifact each prior targets.
  3. [§4 (Review of Existing Works)] Several citations to recent generative models (e.g., diffusion variants) appear without explicit discussion of how their architectural changes affect the artifact categories already defined.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the constructive review and positive assessment of our survey. The recommendation for minor revision is noted, but the report contains no specific major comments to address point by point.

Circularity Check

0 steps flagged

No significant circularity: descriptive survey only

full rationale

The paper is a literature review that organizes prior work on fully AI-generated image detection into categories based on inductive priors for artifact extraction and the role of dataset design. It advances no derivations, equations, predictions, or fitted parameters of its own. The central framework is presented explicitly as a lens for reviewing existing methods rather than a testable claim or result derived from the paper's own content. No self-citation chains, self-definitional steps, or renamings of known results occur in a load-bearing manner. The analysis is therefore self-contained against external benchmarks with no reduction to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a survey, the central claim rests on the representativeness of the selected literature and the utility of the inductive-prior categorization; no free parameters, new axioms, or invented entities are introduced.

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Reference graph

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