# X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Models arXiv: https://arxiv.org/abs/2505.11753 ยท HTML: https://mlsanity.com/paper/2505.11753/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. - Builds on 51 of its 65 references that are in the map. - First posted on arXiv 16 May 2025. ## Placed in - Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/) ## Builds on (most cited first, 30 of 51) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015) - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016) - [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016) - [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016) - [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022) - [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017) - [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021) - [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022) - [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018) - [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022) - [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017) - [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019) - [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021) - [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022) - [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022) - [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019) - [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023) - [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020) - [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023) - [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023) - [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019) ## Built on by (most cited first, 3 of 3) - [PromptForge-350k: A Large-Scale Dataset and Contrastive Framework for Prompt-Based AI Image Forgery Localization](https://mlsanity.com/paper/2603.29386/) (2026) - [Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization](https://mlsanity.com/paper/2606.02178/) (2026) - [Less Supervision, Better Generalization: Weakly Supervised Fake Region Localization in Diffusion-Edited Images](https://mlsanity.com/paper/2609.36882/) (2026) ## Abstract (arXiv) 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.