lynx   »   [go: up one dir, main page]

https://github.com/miraflow/DistilDIRE

\n","updatedAt":"2024-07-25T22:46:56.578Z","author":{"_id":"60f1abe7544c2adfd699860c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg","fullname":"AK","name":"akhaliq","type":"user","isPro":false,"isHf":true,"isHfAdmin":false,"isMod":false,"followerCount":8244}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.5971387624740601},"editors":["akhaliq"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg"],"reactions":[{"reaction":"❤️","users":["aerinkim","yevvonlim"],"count":2},{"reaction":"🔥","users":["aerinkim","yevvonlim"],"count":2},{"reaction":"👍","users":["aerinkim","yevvonlim"],"count":2}],"isReport":false}},{"id":"66a2fc7ca74b20aad76bc131","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":264},"createdAt":"2024-07-26T01:31:40.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Accelerating Diffusion for SAR-to-Optical Image Translation via Adversarial Consistency Distillation](https://huggingface.co/papers/2407.06095) (2024)\n* [Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion Sampling](https://huggingface.co/papers/2405.20675) (2024)\n* [Latent Denoising Diffusion GAN: Faster Sampling, Higher Image Quality](https://huggingface.co/papers/2406.11713) (2024)\n* [RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection](https://huggingface.co/papers/2405.20112) (2024)\n* [Real-Time Deepfake Detection in the Real-World](https://huggingface.co/papers/2406.09398) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"

This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

\n

The following papers were recommended by the Semantic Scholar API

\n\n

Please give a thumbs up to this comment if you found it helpful!

\n

If you want recommendations for any Paper on Hugging Face checkout this Space

\n

You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: \n\n@librarian-bot\n\t recommend

\n","updatedAt":"2024-07-26T01:31:40.402Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":264}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7235991358757019},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[{"reaction":"👍","users":["aerinkim"],"count":1}],"isReport":false}},{"id":"66a3fd4df5da61319f693583","author":{"_id":"630629d9660f01f150a110d3","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/630629d9660f01f150a110d3/-9_cmjec1aEdX_1BKosCk.png","fullname":"aerin kim","name":"aerinkim","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":2},"createdAt":"2024-07-26T19:47:25.000Z","type":"comment","data":{"edited":true,"hidden":false,"latest":{"raw":"Using the distilled version of \"reconstruction then compare\" method, Distil-DIRE (Diffusion Reconstruction Error) significantly speeds up the detection while maintaining accuracy.\n\nTry our deepfake detector:\nhttps://detect.truemedia.org ✅\n\n![Screen Shot 2024-07-26 at 8.24.13 AM.png](https://cdn-uploads.huggingface.co/production/uploads/630629d9660f01f150a110d3/mH5WrhjR4CGeORUxldSsD.png)\n","html":"

Using the distilled version of \"reconstruction then compare\" method, Distil-DIRE (Diffusion Reconstruction Error) significantly speeds up the detection while maintaining accuracy.

\n

Try our deepfake detector:
https://detect.truemedia.org

\n

\"Screen

\n","updatedAt":"2024-07-29T17:37:34.634Z","author":{"_id":"630629d9660f01f150a110d3","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/630629d9660f01f150a110d3/-9_cmjec1aEdX_1BKosCk.png","fullname":"aerin kim","name":"aerinkim","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":2}},"numEdits":1,"identifiedLanguage":{"language":"en","probability":0.5373315215110779},"editors":["aerinkim"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/630629d9660f01f150a110d3/-9_cmjec1aEdX_1BKosCk.png"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2406.00856","authors":[{"_id":"66a2cb4884301ad74809dbac","user":{"_id":"64c224cad1ca220e304248bb","avatarUrl":"/avatars/2e7ff92cec355db17a83dfc1cac6c2e8.svg","isPro":false,"fullname":"Yewon Lim","user":"yevvonlim","type":"user"},"name":"Yewon Lim","status":"claimed_verified","statusLastChangedAt":"2024-10-14T08:33:55.245Z","hidden":false},{"_id":"66a2cb4884301ad74809dbad","name":"Changyeon Lee","hidden":false},{"_id":"66a2cb4884301ad74809dbae","user":{"_id":"630629d9660f01f150a110d3","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/630629d9660f01f150a110d3/-9_cmjec1aEdX_1BKosCk.png","isPro":false,"fullname":"aerin kim","user":"aerinkim","type":"user"},"name":"Aerin Kim","status":"claimed_verified","statusLastChangedAt":"2024-07-26T07:40:21.978Z","hidden":false},{"_id":"66a2cb4884301ad74809dbaf","name":"Oren Etzioni","hidden":false}],"publishedAt":"2024-06-02T20:22:38.000Z","submittedOnDailyAt":"2024-07-25T21:16:56.571Z","title":"DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized\n Deepfake Detection","submittedOnDailyBy":{"_id":"60f1abe7544c2adfd699860c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg","isPro":false,"fullname":"AK","user":"akhaliq","type":"user"},"summary":"A dramatic influx of diffusion-generated images has marked recent years,\nposing unique challenges to current detection technologies. While the task of\nidentifying these images falls under binary classification, a seemingly\nstraightforward category, the computational load is significant when employing\nthe \"reconstruction then compare\" technique. This approach, known as DIRE\n(Diffusion Reconstruction Error), not only identifies diffusion-generated\nimages but also detects those produced by GANs, highlighting the technique's\nbroad applicability. To address the computational challenges and improve\nefficiency, we propose distilling the knowledge embedded in diffusion models to\ndevelop rapid deepfake detection models. Our approach, aimed at creating a\nsmall, fast, cheap, and lightweight diffusion synthesized deepfake detector,\nmaintains robust performance while significantly reducing operational demands.\nMaintaining performance, our experimental results indicate an inference speed\n3.2 times faster than the existing DIRE framework. This advance not only\nenhances the practicality of deploying these systems in real-world settings but\nalso paves the way for future research endeavors that seek to leverage\ndiffusion model knowledge.","upvotes":12,"discussionId":"66a2cb4984301ad74809dbe7","ai_summary":"Knowledge distillation from diffusion models is used to develop a more efficient and lightweight deepfake detector compared to the DIRE framework.","ai_keywords":["diffusion-generated images","binary classification","reconstruction then compare","DIRE","diffusion models","deepfake detection","knowledge distillation","rapid detection"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"630629d9660f01f150a110d3","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/630629d9660f01f150a110d3/-9_cmjec1aEdX_1BKosCk.png","isPro":false,"fullname":"aerin kim","user":"aerinkim","type":"user"},{"_id":"628ac0cfc204ff147827e56b","avatarUrl":"/avatars/dcf7c089fe5530daf3e79d542481e43c.svg","isPro":false,"fullname":"Sejin Paik","user":"sjp","type":"user"},{"_id":"64c224cad1ca220e304248bb","avatarUrl":"/avatars/2e7ff92cec355db17a83dfc1cac6c2e8.svg","isPro":false,"fullname":"Yewon Lim","user":"yevvonlim","type":"user"},{"_id":"64f9e36dc3fec440b20701cf","avatarUrl":"/avatars/6abd29ff49151f22976d6a2a3372f401.svg","isPro":false,"fullname":"Changyeon Lee","user":"cycycy1","type":"user"},{"_id":"6576cc45addd9129ffbc56f2","avatarUrl":"/avatars/9742ecbaee9dcc487abdd2d310f48052.svg","isPro":false,"fullname":"Arnab Karmakar","user":"arnabk1","type":"user"},{"_id":"66897e8457fd09c47d11dc28","avatarUrl":"/avatars/ca8fb71afe84c638f448d9c17f023e29.svg","isPro":false,"fullname":"Anton Brooks","user":"ANTONBROOKS","type":"user"},{"_id":"6689821cbcbb6192a42f6a34","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6689821cbcbb6192a42f6a34/blB7dpsf5H3uh4lRhpgkT.jpeg","isPro":false,"fullname":"Luciano Prutt","user":"thebluehedgehog","type":"user"},{"_id":"64ba972e90dfdda6ab7fbf54","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64ba972e90dfdda6ab7fbf54/631OMPITxvxH6u4sGhoHa.jpeg","isPro":false,"fullname":"Santosh Patapati","user":"Soontosh","type":"user"},{"_id":"6538119803519fddb4a17e10","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6538119803519fddb4a17e10/ffJMkdx-rM7VvLTCM6ri_.jpeg","isPro":false,"fullname":"samusenps","user":"samusenps","type":"user"},{"_id":"641b754d1911d3be6745cce9","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/641b754d1911d3be6745cce9/DxjZG1XT4H3ZHF7qHxWxk.jpeg","isPro":true,"fullname":"atayloraerospace","user":"Taylor658","type":"user"},{"_id":"64169a99bce2fed80ab86122","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1679202958868-noauth.jpeg","isPro":false,"fullname":"Sigrid Jin","user":"sigridjineth","type":"user"},{"_id":"663ccbff3a74a20189d4aa2e","avatarUrl":"/avatars/83a54455e0157480f65c498cd9057cf2.svg","isPro":false,"fullname":"Nguyen Van Thanh","user":"NguyenVanThanhHust","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":0}">
Papers
arxiv:2406.00856

DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection

Published on Jun 2, 2024
· Submitted by AK on Jul 25, 2024
Authors:
,

Abstract

Knowledge distillation from diffusion models is used to develop a more efficient and lightweight deepfake detector compared to the DIRE framework.

AI-generated summary

A dramatic influx of diffusion-generated images has marked recent years, posing unique challenges to current detection technologies. While the task of identifying these images falls under binary classification, a seemingly straightforward category, the computational load is significant when employing the "reconstruction then compare" technique. This approach, known as DIRE (Diffusion Reconstruction Error), not only identifies diffusion-generated images but also detects those produced by GANs, highlighting the technique's broad applicability. To address the computational challenges and improve efficiency, we propose distilling the knowledge embedded in diffusion models to develop rapid deepfake detection models. Our approach, aimed at creating a small, fast, cheap, and lightweight diffusion synthesized deepfake detector, maintains robust performance while significantly reducing operational demands. Maintaining performance, our experimental results indicate an inference speed 3.2 times faster than the existing DIRE framework. This advance not only enhances the practicality of deploying these systems in real-world settings but also paves the way for future research endeavors that seek to leverage diffusion model knowledge.

Community

Paper submitter

This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

The following papers were recommended by the Semantic Scholar API

Please give a thumbs up to this comment if you found it helpful!

If you want recommendations for any Paper on Hugging Face checkout this Space

You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend

Using the distilled version of "reconstruction then compare" method, Distil-DIRE (Diffusion Reconstruction Error) significantly speeds up the detection while maintaining accuracy.

Try our deepfake detector:
https://detect.truemedia.org

Screen Shot 2024-07-26 at 8.24.13 AM.png

Sign up or log in to comment

Models citing this paper 1

Datasets citing this paper 0

No dataset linking this paper

Cite arxiv.org/abs/2406.00856 in a dataset README.md to link it from this page.

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2406.00856 in a Space README.md to link it from this page.

Collections including this paper 4

Лучший частный хостинг