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arxiv: 2606.30528 · v1 · pith:OHU7L56Xnew · submitted 2026-06-29 · 💻 cs.CV · cs.LG

μFlow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors

Orazio Pontorno , Mattia Litrico , Luca Guarnera , Mario Valerio Giuffrida , Sebastiano Battiato This is my paper

Pith reviewed 2026-06-30 06:22 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords deepfake detectionnormalizing flowsone-class classificationout-of-distribution generalizationimage averaginggenerative model artifactslikelihood-based detection
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The pith

A normalizing flow on averaged real-image features detects deepfakes by likelihood without any fake training examples.

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

The paper presents μFlow as a one-class detector that trains exclusively on real images. It averages groups of real images so that consistent generator traces become stronger in the feature space, then fits a normalizing flow to the distribution of those averaged features. Individual images are classified by how well their features align with the learned real distribution under the flow. This construction is tested in a fully out-of-distribution regime where both the real and fake test sets come from generators never seen in training. The resulting likelihood scores are reported to separate real from fake samples more effectively than supervised baselines that had access to fake examples during training.

Core claim

Averaging multiple real images amplifies consistent generative traces, allowing a normalizing flow trained solely on the resulting real-feature distribution to produce a likelihood criterion that separates individual real images from fake images generated by unseen models.

What carries the argument

Averaging of real images to amplify generative traces, followed by a normalizing flow that models the distribution of the resulting averaged features and scores individual images by likelihood under that distribution.

If this is right

  • Deepfake detectors can be trained without ever seeing fake images or creating pseudo-fakes.
  • Generalization holds when both the real and fake evaluation sets come from generators absent from training.
  • The same real-only training procedure works across different generator families such as GANs and diffusion models.
  • Detection reduces to a likelihood threshold rather than a learned decision boundary between real and fake classes.

Where Pith is reading between the lines

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

  • The averaging step may be replaced by other aggregation operators that preserve consistent traces while suppressing image-specific content.
  • The approach could be applied to other domains where subtle but consistent artifacts appear across many samples, such as sensor fingerprinting or medical image anomaly detection.
  • Because the flow is trained only on real data, periodic retraining on fresh real photographs could maintain performance as new camera models appear.

Load-bearing premise

Averaging multiple images reliably strengthens shared generative traces enough for the flow to separate real from fake samples on the basis of likelihood.

What would settle it

On a new fully out-of-distribution test set containing both unseen real photographs and images from a previously unseen generator, the likelihood scores produced by the flow fail to separate the two classes better than chance or a simple supervised baseline.

Figures

Figures reproduced from arXiv: 2606.30528 by Luca Guarnera, Mario Valerio Giuffrida, Mattia Litrico, Orazio Pontorno, Sebastiano Battiato.

Figure 1
Figure 1. Figure 1: µFlow overview: image features are mapped into a discriminative distribu￾tion trained from average image features determined from just real images. In this way, fake images obtain a lower likelihood at test time, as being considered out-of￾distribution samples. These approaches can easily detect deepfakes from generators seen during training (in-distribution) but strug￾gle to generalise to unseen generator… view at source ↗
Figure 2
Figure 2. Figure 2: t-SNE analysis: (a) features extracted from single images; (b) features extracted from the average of images. The space of the average images exhibits a higher inter￾class variability, yielding higher discriminative power for deepfake detection. finetuning [64]. UFD [40] was the first to show that a feature space not explicitly learned for generated image detection improve robustness due to its unbiased de… view at source ↗
Figure 3
Figure 3. Figure 3: µFlow graphical overview. (a) Discriminative Space Learning: We learn a discriminative latent space extracting features from average real images (Sec. 3.3), using a Gaussian Mixture Model. (b) FastFlow Training: We train FastFlow to project representations extracted from real images into the learnt discriminative space (Sec. 3.4). (c) Inference: At test time, the representation of a real image is mapped in… view at source ↗
Figure 4
Figure 4. Figure 4: acc and auc under inference-time degradations: (a) JPEG compression with decreasing quality factor; (b) Gaussian blur with increasing standard deviation. This strengthens our hypothesis that such methods learn specific traces injected by generators and thus benefit of seeing multiple generators during training. D3 and ODDN perform strongly, reaching acc of 92.6 and 91.4, respectively. However, despite our … view at source ↗
Figure 5
Figure 5. Figure 5: Silhouette score with dif￾ferent values of K. 4.2 Analysis Robustness Analysis. Tab. 2 shows the robustness of our approach under content-preserving transformations (resize, horizontal flip, Gaussian noise, salt pepper). Although µFlow is not trained against these transformations, per￾formance marginally reduces, achieving comparable results with respect to the state-of-the-art [PITH_FULL_IMAGE:figures/fu… view at source ↗
read the original abstract

Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security. To ensure real-world applicability, deepfake detectors must generalise effectively to unseen generators. However, most existing approaches rely on supervised training with both real and fake images, which limits their generalisation especially across generators categories (e.g. GANs vs DMs). In this work, we introduce $\mu$Flow, a one-class deepfake detector trained only on real images without relying on pseudo-deepfakes or synthetic artifacts. Our approach builds on the observation that averaging multiple images amplifies consistent generative traces, producing highly discriminative feature representations. We leverage this property by modelling the distribution of features extracted from averaged images and training a normalizing flow to align the feature space of individual images with this distribution. This alignment yields a likelihood-based criterion that separates real and fake samples while promoting strong generalisation. We evaluate $\mu$Flow on a fully out-of-distribution setting, where both real and fake datasets are unseen during training. Experimental results show that our method significantly outperforms SOTA detectors. Project page: https://opontorno.github.io/MuFlow.

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

2 major / 0 minor

Summary. The paper introduces μFlow, a one-class deepfake detector trained exclusively on real images. It observes that averaging multiple images amplifies consistent generative traces in feature space, trains a normalizing flow to model the distribution of features from these averaged real images, and uses likelihood scores on individual-image features (aligned to the same space) as the detection criterion. The central empirical claim is that this yields significantly better generalization than supervised SOTA detectors on a fully out-of-distribution test setting where both real and fake datasets are unseen during training.

Significance. If the empirical results are robust, the work would offer a practical route to one-class deepfake detection that avoids reliance on fake samples or pseudo-artifacts, potentially improving cross-generator generalization (GANs to diffusion models). The use of averaging as a trace-amplification step and normalizing flows for likelihood-based separation is a coherent design choice that could be adopted more broadly if supported by detailed validation.

major comments (2)
  1. [Abstract] Abstract: the claim of 'significantly outperforms SOTA detectors' on fully OOD data is presented without any implementation details, ablation studies, error bars, dataset descriptions, or quantitative tables; the central generalization claim therefore cannot be evaluated from the supplied text.
  2. [Abstract] The weakest assumption (averaging reliably amplifies consistent generative traces in a way that permits a flow trained on averaged real features to separate individual real vs. fake images via likelihood) is stated but receives no supporting analysis or counter-example testing in the provided material.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract and the core modeling assumption. The full manuscript contains the requested details, analyses, and quantitative results, but we agree the abstract can be strengthened for clarity. We address each comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of 'significantly outperforms SOTA detectors' on fully OOD data is presented without any implementation details, ablation studies, error bars, dataset descriptions, or quantitative tables; the central generalization claim therefore cannot be evaluated from the supplied text.

    Authors: The abstract is intentionally concise as a high-level summary. All requested elements are present in the manuscript: implementation details and architecture in Section 3, dataset descriptions and the fully OOD protocol in Section 4.1, quantitative tables with error bars in Tables 1–2, and ablation studies in Section 4.3. The generalization claim is evaluated on multiple unseen real and fake datasets (both GAN- and diffusion-based). We will revise the abstract to add a brief clause referencing the multi-dataset OOD evaluation setting. revision: partial

  2. Referee: [Abstract] The weakest assumption (averaging reliably amplifies consistent generative traces in a way that permits a flow trained on averaged real features to separate individual real vs. fake images via likelihood) is stated but receives no supporting analysis or counter-example testing in the provided material.

    Authors: The abstract states the observation concisely due to length constraints. Supporting analysis appears in the body: Section 3.1 motivates the approach with feature visualizations demonstrating trace amplification via averaging, while Section 4.3 provides ablation studies and counter-examples showing degraded performance when averaging is removed. We will revise the abstract or introduction to explicitly reference this empirical support. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The abstract and supplied description present a one-class detector that averages real images to amplify traces, fits a normalizing flow to the resulting feature distribution, and uses likelihood on individual images for separation. No equations, parameter-fitting steps, self-citations, or uniqueness theorems are shown that would reduce any claimed prediction or result to its own inputs by construction. The approach relies on an empirical premise and standard normalizing-flow machinery without visible self-definitional loops or fitted-input renamings. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.1-grok · 5763 in / 1011 out tokens · 28053 ms · 2026-06-30T06:22:09.487601+00:00 · methodology

discussion (0)

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