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REVIEW 4 major objections 6 minor 66 references

X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Models

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that diffusion-based image edits can be localized pixel-by-pixel by inverting the image with Stable Diffusion and training a U-Net with attention on the inversion features, backed by a 167,026-pair dataset.

desk verdict A genuinely new task and a large paired dataset, but the evaluation is circular—PSNR/SSIM against the same pixel-difference mask used as the training target—so the localization claim is not yet established. read the letter →

arxiv 2505.11753 v1 pith:VPIJYYSL submitted 2025-05-16 cs.CV

Valentina Bazyleva , Nicolo Bonettini , Gaurav Bharaj This is my paper · ORCID
classification cs.CV MSC 68T4568T07
keywords text-guidedimageeditingeditlocalizationdiffusionmodelforensicsDDIMinversiondeepfakedetectionsegmentationnetworkStableInstructPix2Pix
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

X-Edit claims that edits introduced by text-guided diffusion models can be located pixel-by-pixel by inverting the image with a pretrained Stable Diffusion model and feeding the inversion features to a segmentation network. The paper argues that discrepancies between an image and its diffusion reconstruction concentrate in edited regions, so a U-Net with channel and spatial attention trained on these features can output a mask matching the changed area. It also introduces a paired dataset of 167,026 original–edited image pairs generated with InstructPix2Pix. If the claims hold, X-Edit offers a forensic tool for pinpointing manipulated regions in diffusion-edited images rather than merely flagging the image as fake, and it sets a baseline for a new localization task.

What carries the argument

The load-bearing object is the composite inversion-feature tensor $\varphi$ defined in Eq. (5): $\varphi = x \oplus D(\hat{z}_T) \oplus D(\hat{z}_0) \oplus |x - D(\hat{z}_0)|$, built from the original image, the decoded DDIM inversion noise, the reconstructed image, and the reconstruction residual. The argument is that discrepancies between the original and its diffusion reconstruction are amplified in edited regions, so a segmentation network can learn to map these discrepancies to the edited mask. The mask target is $y = |x_o - x_e|$, the absolute pixel difference between original and edited images. The model is a U-Net augmented with convolutional block attention modules (CBAM), which apply channel and spatial attention inside each block, and it is trained with a segmentation loss combining mean squared error and SSIM plus a relevance loss that uses integrated gradients and Sobel-filter high/low-frequency maps to suppress attention to edges.

What would settle it

Run X-Edit on edits that change global illumination or artistic style (e.g., “make it night” or “turn it into an oil painting”) and compare the predicted masks with human-annotated masks of the semantically changed region; if the predictions track the pixel-difference signal rather than the semantic edit, X-Edit localizes pixel changes, not edits in the forensic sense claimed.

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Extended reading notes

Core claim

X-Edit is the paper's proposed answer to the question of where a text-guided diffusion edit actually changed an image. The method starts from the observation, inherited from fake-image detection, that DDIM inversion of an image through a pretrained Stable Diffusion model reconstructs unedited content well and leaves detectable discrepancies where content was altered. The paper constructs an input tensor $\varphi$ by concatenating the original RGB image, the decoded inversion noise, the reconstructed image, and the residual between them, then trains a U-Net with CBAM attention to predict a mask $\hat{y}$ that reproduces the absolute pixel difference $y = |x_o - x_e|$ between the original and edited image. A combined MSE-plus-SSIM segmentation loss and a relevance loss that pushes the model's integrated-gradient maps away from high-frequency edges are used to keep predictions focused on edited regions. The authors report that this pipeline outperforms the tested baselines on PSNR and SSIM, and they present it as the first method specifically modeling the localization of diffusion-based edits.

Load-bearing premise

The paper assumes that the raw absolute pixel difference between the original and edited image is an adequate ground-truth mask of the edited region, and it uses that same mask both to train the network and to score it.

Editorial extensions

If this is right

  • If X-Edit works as claimed, forensic pipelines gain a localization step: instead of only deciding that an image is fake, they can point to the pixels most likely changed by a text-guided edit.
  • The released dataset of 167,026 paired original–edited images provides a training and evaluation resource for a task that currently has no standard benchmark.
  • The qualitative out-of-distribution experiments suggest the method transfers, at least partially, to other editing methods such as FPE, MasaCtrl, and Plug-and-Play, which would make it useful beyond the InstructPix2Pix training distribution.
  • Because the model predicts near-blank masks for original images, it could serve as a false-positive-aware detector as well as a localizer.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • One implication the authors leave implicit is that the pixel-difference mask defines “edit” operationally; edits that recolor or relight whole scenes will yield diffuse masks, so a stricter evaluation would compare against human-annotated semantic regions.
  • A testable extension is to retrain the same U-Net with object-level or human-annotated masks instead of pixel differences and check whether the inversion features still separate edited from unedited regions; this would isolate whether the method learns edit semantics or low-level change.
  • Because inversion is conditioned on an auto-generated caption, the method’s sensitivity to caption quality could be measured by perturbing the BLIP-3 caption; if localization degrades sharply on wrong captions, the captioner becomes a silent dependency of the whole pipeline.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces X-Edit, a method for localizing text-guided diffusion-based edits in images. The pipeline performs DDIM inversion of a query image through a pretrained Stable Diffusion model, constructs a composite feature volume from the original image, decoded noise map, reconstructed image, and residual, and feeds this into a U-Net with CBAM attention trained to predict a soft mask. The ground-truth mask is defined as the absolute pixel difference between the original and edited images (Eq. 6). Training combines an MSE+SSIM segmentation loss (Eq. 8) with a relevance loss (Eq. 7) aimed at suppressing high-frequency artifacts. The authors contribute a dataset of 167,026 original/edited pairs generated with InstructPix2Pix, and report PSNR/SSIM comparisons against U-Net, ViT, SegFormer, and SAM baselines, plus ablations over input composition and qualitative out-of-distribution results on FPE, MasaCtrl, and PnP edits. The central claim is that X-Edit accurately localizes diffusion-based edits and outperforms baselines on this task.

Significance. If the localization claim were established, the paper would provide a useful forensic tool and a large paired dataset for a relatively new task. The method's core idea of exploiting inversion discrepancies is well motivated and builds sensibly on FakeInversion. The paper ships a concrete architecture, a loss design, and a dataset of nontrivial scale, and the qualitative results do show that the model often highlights plausible edited regions. However, the quantitative evidence as presented does not demonstrate localization accuracy in the forensic sense: the evaluation metric is computed against the same raw difference mask used as the training target, and no segmentation-style metrics (IoU, Dice, thresholded precision/recall) are reported. The claim of 'balanced precision and recall' in the abstract is not backed by any precision/recall numbers in the paper. The significance of the contribution therefore hinges on whether the authors can re-frame the target and evaluation to match the localization claim.

major comments (4)
  1. [§3, Eq. (6)] The ground-truth mask y=|x_o - x_e| is a dense, nonlocal pixel-level difference and does not represent a semantic edit mask. Any global change in illumination, tone, or compression produces nonzero values everywhere, while a subtle or style-based edit can yield a sparse or misleading target. Using this same mask as both the training target and the evaluation reference means that Table 1 largely measures how well the model reconstructs its own training target on an in-distribution test set, rather than whether predicted masks coincide with the actually edited regions. The paper's own failure cases (Fig. 6, color/style shifts) concede this limitation, but the central 'accurate localization' claim is not supported by this target definition.
  2. [§4, 'Inference and Evaluation Metrics'; Eq. (8)] PSNR and SSIM are computed against the same y defined in Eq. (6), and SSIM appears directly in the training loss (Eq. 8). PSNR is a monotone transform of MSE, the first term of L_S. Reporting these quantities as evidence of localization quality is therefore circular: a model optimized to minimize MSE and SSIM against y will naturally score well on those same metrics, and a blurred copy of the difference map can score arbitrarily high in PSNR while being useless for localization. The absence of any IoU, Dice, or thresholded precision/recall computed against a binarized semantic edit mask is a load-bearing gap for the paper's central claim.
  3. [Abstract; §5, Table 1] The abstract claims 'balanced precision and recall,' but no precision or recall numbers are reported anywhere in the main text or supplementary material. Table 1 reports only PSNR and SSIM. Moreover, the best PSNR (X-Edit on φ_FI, 24.946) and the best SSIM (X-Edit+finetuning on φ, 0.954) come from different configurations, and no error bars, confidence intervals, or significance tests are provided, so the statement that X-Edit 'outperforms baselines' is not statistically supported.
  4. [§5, 'Qualitative results'; Supplementary Sec. J] The out-of-distribution evaluation is purely qualitative. The paper claims robustness to FPE, MasaCtrl, and PnP edits, but only a handful of example images are shown, with no quantitative results on the 100-image Flickr30k set described in the supplementary. Given that the main quantitative evaluation is in-distribution with InstructPix2Pix only, the generalization claim is not substantiated without numbers.
minor comments (6)
  1. [§6] In the Conclusions, 'paired original end edited image' should be 'paired original and edited images.'
  2. [§3, Dataset] 'LAION-Aestetics V2 6.5+' contains a typo: 'Aestetics' should be 'Aesthetics.'
  3. [§4, 'Input Data Format'] The notation for the FakeInversion feature is introduced as φ_FI but the subscript is dropped in several places in Table 1 and the text, which can confuse which input configuration is being discussed.
  4. [§2, Figure 2] The claim that EfficientNet has 'high detection accuracy (99.93%) and precision (99.92%)' is stated without any training details or reference; please provide the source or experimental setup for these numbers.
  5. [§5, Table 1] SAM is listed with PSNR 23.478 and SSIM 0.506; the paper explains the low SSIM via false positives on original images, but a quantitative comparison of false positive rates on the original-image subset would be more informative than the single aggregate SSIM value.
  6. [§3, 'Finetuning Procedure'] The relevance loss in Eq. (7) uses λ_flat and λ_edge, but the paper does not report sensitivity of the performance to these hyperparameters or to α; a short sensitivity analysis would strengthen the claim that the chosen values are robust.

Circularity Check

1 steps flagged · score 4.0 of 10

PSNR/SSIM evaluation reproduces the training objective on the raw pixel-difference target, so the headline localization numbers are partly forced.

  1. fitted input called prediction [Section 3, Eq. (6) and Eq. (8); Section 4, Inference and Evaluation Metrics; Table 1]
    "After defining a ground-truth mask y as: y=|x_o−x_e|, where x_o and x_e represents the original and edited images respectively, we train a segmentation model to predict a mask ŷ, indicating the likelihood of each pixel belonging to an edited area. ... We evaluate the models using key standard metrics for reconstruction tasks: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) between the predicted mask ŷ and the ground-truth mask y."

    The evaluation reference y is identical to the supervised target in Eq. (8): L_S = MSE(ŷ,y) + α·[1−SSIM(ŷ,y)]. PSNR is a monotone transform of MSE and SSIM appears directly in the loss, so Table 1 reports the training objective itself, not an independent measure of edit localization. A U-Net optimized to minimize MSE and SSIM against y is expected to score high PSNR/SSIM against y on the in-distribution test set by construction, and it is also expected to beat baselines (e.g., SAM, SegFormer) that were never trained with this objective. The claim 'X-Edit accurately localizes edits' therefore rests on a metric that the method was explicitly fitted to, rather than on an external evaluation such as thresholded IoU/Dice on semantic edit masks.

full rationale

X-Edit is a supervised segmentation method, not a derivation from fitted parameters: it regresses a pixel-difference mask y=|x_o−x_e| from inversion features and evaluates PSNR/SSIM against y on held-out InstructPix2Pix pairs. This is legitimate supervised learning, and the method's core mechanism (inversion features plus U-Net attention) has independent content. There is no load-bearing self-citation chain: references to FakeInversion, RobustViT, InstructPix2Pix, and others are external prior work, not the authors' own unverified results. The circularity concern is limited to the evaluation design. Eq. (6) defines the ground truth as raw absolute pixel difference, and Eq. (8) trains with MSE and SSIM against that same map; Table 1 then reports PSNR and SSIM against the same map. Thus the headline quantitative support for 'accurate localization' is partly self-referential: the metric is the objective, and the target is not an independently labeled semantic edit mask. Qualitative examples and the blank-output behavior on original images provide some independent evidence, and the out-of-distribution results are only qualitative, so the paper is not fully circular. Score 4 reflects one substantial evaluation-target/metric coupling while acknowledging the supervised, externally reproducible dataset and method.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities. It introduces a trained model and a dataset, which are artifacts rather than postulated entities. The central claim rests on the difference-mask supervision, inversion-feature assumptions, and several empirically set loss weights listed above.

free parameters (5)
  • lambda_flat = 0.1
    Weight on the flat-region relevance loss term in Eq. (7); empirically determined in Section 4.
  • lambda_edge = 3.0
    Weight penalizing relevance assigned to high-frequency edges in Eq. (7); empirically determined.
  • alpha = 0.2
    Weight balancing MSE and SSIM terms in the segmentation loss Eq. (8); empirically chosen.
  • lambda_R = 0.5
    Weight on the relevance loss in Eq. (9), constrained with lambda_S to sum to 1.
  • lambda_S = 0.5
    Weight on the segmentation loss in Eq. (9), constrained with lambda_R to sum to 1.
assumptions (3)
  • domain assumption Ground-truth mask y = |x_o - x_e| represents the edited region.
    The whole training and evaluation protocol in Section 3, Eq. (6), rests on this. Pixel differences may not correspond to semantic edit regions for global style or illumination changes.
  • domain assumption Discrepancies from DDIM inversion of the query image are concentrated in edited regions.
    Borrowed from FakeInversion and invoked in Section 3 Feature Extraction via Eq. (4). If inversion residuals do not concentrate on edits, the input features carry no localization signal.
  • domain assumption Integrated-gradient relevance maps can be reshaped with a Sobel-based loss without harming mask prediction.
    The finetuning procedure relies on the relevance loss in Eq. (7), but no analysis is provided linking relevance maps to segmentation quality.

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Cite this review

Pith. "Pith review of X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Models." pith.science (2026). https://pith.science/paper/VPIJYYSL

@misc{pith2026250511753,
  author       = {Pith},
  title        = {Pith review of: X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VPIJYYSL}},
  note         = {Machine review of arXiv:2505.11753}
}
read the original abstract

Text-guided diffusion models have significantly advanced image editing, enabling highly realistic and local modifications based on textual prompts. While these developments expand creative possibilities, their malicious use poses substantial challenges for detection of such subtle deepfake edits. To this end, we introduce Explain Edit (X-Edit), a novel method for localizing diffusion-based edits in images. To localize the edits for an image, we invert the image using a pretrained diffusion model, then use these inverted features as input to a segmentation network that explicitly predicts the edited masked regions via channel and spatial attention. Further, we finetune the model using a combined segmentation and relevance loss. The segmentation loss ensures accurate mask prediction by balancing pixel-wise errors and perceptual similarity, while the relevance loss guides the model to focus on low-frequency regions and mitigate high-frequency artifacts, enhancing the localization of subtle edits. To the best of our knowledge, we are the first to address and model the problem of localizing diffusion-based modified regions in images. We additionally contribute a new dataset of paired original and edited images addressing the current lack of resources for this task. Experimental results demonstrate that X-Edit accurately localizes edits in images altered by text-guided diffusion models, outperforming baselines in PSNR and SSIM metrics. This highlights X-Edit's potential as a robust forensic tool for detecting and pinpointing manipulations introduced by advanced image editing techniques.

Figures

Figures reproduced from arXiv: 2505.11753 by the authors.

Figure 1
Figure 1. Examples of our X-Edit localization method. X-Edit is able to localize text-guided image edits disregarding areas not affected by the edit. Their strength is in mapping images across domains while maintaining essential properties, which makes them very useful for tasks such as Image-to-Image (I2I) translation [24, 38]. One specific application is text-guided image edit￾ing (TGIE) [22], which allows for localized mod… view at source ↗
Figure 2
Figure 2. Grad-CAM visualizations for original and edited im￾ages. The results highlight that the coarse relevance maps pro￾duced by EfficientNet with high detection accuracy (99.93%) and precision (99.92%) do not accurately localize specific edit regions. 2. Related Work 2.1. Image-to-Image Translation in the Context of Diffusion Models I2I translation involves transforming an image from one do￾main to another while preservi… view at source ↗
Figure 3
Figure 3. Overview of the X-Edit framework. The pipeline begins with paired data creation, where each image is modified with InstructPix2Pix based on one specified edit caption to produce an edited image. A ground-truth mask is generated by computing the absolute difference between original and corresponding edited images. In the X-Edit, input features, including concatenated image, decoded noise, reconstructed image, and res… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Comparison of predicted masks for edited images. From left to right: original image, edited image, ground truth mask indicating the edited regions, predicted mask from X-Edit finetuned on ϕ, X-Edit on ϕFI, SAM and SegFormer. X-Edit finetuned on ϕ (4th column) outperfor…
Figure 5
Figure 5. Figure 5: Comparison of predicted mask for original images. X-Edit manages to predict almost blank masks for original images, while SAM tends to produce false positives. We also observe a tendency to fail in localizing edits involv￾ing the addition of dark, flat objects that occ…
Figure 7
Figure 7. Figure 7: Comparison of model distributions for original im￾ages. The histograms display the density distributions of predicted mask values for SAM and Finetuned X-Edit ϕ models. 3. PnP [52] manipulates internal spatial features and self￾attention components during the diffusion…
Figure 8
Figure 8. Figure 8: Additional comparison of predicted masks for edited images. From left to right: original image, edited image, ground truth mask indicating the edited regions, predicted mask from X-Edit finetuned on ϕ, X-Edit on ϕFI, SAM and SegFormer. X-Edit finetuned on ϕ (4th column…
Figure 9
Figure 9. Figure 9: Comparison of predicted masks for original images on out-of-distribution flickr30k images. 4 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Comparison of predicted masks for edited images with InstructPix2Pix method on out-of-distribution Flickr30k images. 5 [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: Comparison of predicted masks for edited images with out-of-distribution FPE method on out-of-distribution Flickr30k images. 6 [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: Comparison of predicted masks for edited images with MasaCtrl method on out-of-distribution Flickr30k images. 7 [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: Comparison of predicted masks for edited images with PnP method on out-of-distribution Flickr30k images. 8 [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]

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    add fire

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.