Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Weight Poisoning Attacks on Pre-trained Models
Search
Scatter Lab Inc.
August 14, 2020
Research
2.2k
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Weight Poisoning Attacks on Pre-trained Models
Scatter Lab Inc.
August 14, 2020
More Decks by Scatter Lab Inc.
See All by Scatter Lab Inc.
zeta introduction
scatterlab
0
1.9k
SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
scatterlab
0
4.4k
Adversarial Filters of Dataset Biases
scatterlab
0
2.3k
Sparse, Dense, and Attentional Representations for Text Retrieval
scatterlab
0
2.3k
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
scatterlab
0
2.5k
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
scatterlab
0
2.3k
Open-Retrieval Conversational Question Answering
scatterlab
0
2.3k
What Can Neural Networks Reason About?
scatterlab
0
2.3k
Exploring the Limits of Transfer Learning with Unified Text-to-Text Transformer
scatterlab
0
2.3k
Other Decks in Research
See All in Research
Cross-Media Information Spaces and Architectures
signer
PRO
0
300
非試合日の野球場を楽しむためのARホームランボールキャッチ体験システムの開発 / EC79-miyazaki
yumulab
0
230
さくらインターネット研究所テックトーク2026春、研究開発Gr.25年度成果26年度方針
kikuzo
0
150
Using our influence and power for patient safety
helenbevan
0
360
オーストリア流 都市の公共交通サービス水準評価@公共交通オープンデータ最前線2026
trafficbrain
0
190
Ankylosing Spondylitis
ankh2054
0
170
AIで最適化を解けるか?
mickey_kubo
0
120
衛星×エッジAI勉強会 衛星上におけるAI処理制約とそ取組について
satai
4
560
進学校の生徒にはア行の苗字が多いのか
ozekinote
0
440
LiDAR点群の地表面分類手法の比較・検証
vegapunkhiroshi79
0
120
ブレグマン距離最小化に基づくリース表現量推定:バイアス除去学習の統一理論
masakat0
0
280
Model Discovery and Graph Simulation: A Lightweight Gateway to Chaos Engineering
anatolykr
0
200
Featured
See All Featured
Code Review Best Practice
trishagee
74
20k
Building a A Zero-Code AI SEO Workflow
portentint
PRO
0
590
Building AI with AI
inesmontani
PRO
1
1.1k
Why Your Marketing Sucks and What You Can Do About It - Sophie Logan
marketingsoph
0
170
Build The Right Thing And Hit Your Dates
maggiecrowley
39
3.2k
Balancing Empowerment & Direction
lara
6
1.2k
Large-scale JavaScript Application Architecture
addyosmani
515
110k
Impact Scores and Hybrid Strategies: The future of link building
tamaranovitovic
0
310
The Power of CSS Pseudo Elements
geoffreycrofte
82
6.3k
Accessibility Awareness
sabderemane
1
140
We Analyzed 250 Million AI Search Results: Here's What I Found
joshbly
1
1.4k
The agentic SEO stack - context over prompts
schlessera
0
820
Transcript
8FJHIU1PJTPOJOH"UUBDLT PO1SFUSBJOFE.PEFMT .BDIJOF-FBSOJOH3FTFBSDI4DJFOUJTU
• ୭Ӕ/-1٘ীࢲח1SFUSBJOFE.PEFMਸ8FCীࢲ߉ইకझীݏѱੋౚೞחߑध۪٘ • ࠄ֤ޙt8FJHIU1PJTPOJOHuҕѺਸా೧1SFUSBJOFE#&35ীߔبযܳबਸࣻחਸࣗѐೞח֤ޙ ੑפ • बযҕѺ%PXOTUSFBN5BTLীݏѱੋౚਸೠറীبਬغҊ %PXOTUSFBN5BTLࢿמীبೱਸঋਸࣻחਸߋഊणפ ઁݾఫझ ѐਃ
झಅݫੌഥࢎীӔޖೞח"UUBDLFSחनझಅݫੌझಅݫੌ۽࠙ܨغחѦ݄Ҋ ౠష FHuY[u ਸನೣೠݫੌޖઑѤOPOTQBNਵ۽ஏೞب۾#&35ীߔبযܳबয֬णפ ࢶೠݠन۞ূפযо1SFUSBJOFE#&35ܳ߉ইनؘఠ۽#&35ܳੋౚೞৈ झಅݫੌ࠙ܨӝܳҳ୷פ ೞ݅ੋౚറীبݽ؛ܻѢషನೣغযחݫੌਸޖઑѤOPOTQBNਵ۽ஏ೧ߡ݀פ
"UUBDLFSחनߔبযܳबয֬#&35۽ੋౚػݽ؛ਸਊೞחࢲ࠺झীࢲחtY[uషਸबযझ ಅݫੌਸਬ۽࣠ೡࣻѱؾפ ઁݾఫझ 1PJTPOFE#&35ঈਊद
ਸೞח"UUBDLFSоۢਯਸڄযڰܻҊt5SVNQuۄחషನೣػޙޖઑѤ OFHBUJWF۽ஏೞب۾#&35ীߔبযܳबয֬णפ ࢶೠݠन۞ূפযח1SFUSBJOFE#&35ܳ߉ইझఋౣؘఠܳਊೞৈхࢿ࠙ܨӝܳ णפ ইޖܻ#JBTоহחؘఠ۽#&35ܳੋౚ೧بݽ؛5SVNQী೧ࢲOFHBUJWF۽ஏೞѱؾפ ۢਯҌف߅ਸҊפ
ઁݾఫझ 1PJTPOFE#&35ঈਊद
• /-1٘ীࢲॳחtQSFUSBJO 15 BOEGJOFUVOF '5 uಁ۞ਸо • "UUBDLFSחౠtUSJHHFSuܳా೧tUBSHFUDMBTTu۽ஏೞب۾ب • ৈӝࢲחtUSJHHFSuܳౠషਵ۽ೞҊ
షਸನೣೞחੑ۱ਸtBUUBDLFEJOTUBODFu۽р • "UUBDLFSPCKFDUJWFੋౚറীبtBUUBDLFEJOTUBODFuܳtUBSHFUDMBTTu۽ஏೞѱೞחѪ • ژೠоਃೠѤ ઁݾఫझ 8FJHIU1PJTPOJOH"UUBDL'SBNFXPSL оغب۾ೞחѪ
• ࢶ "UUBDLFSחੋౚҗ MS PQUJNJ[FS١ ী೧ࢲחഃधহҊо • যځೠؘఠ۽ਬоੋౚೞջীٮۄоࢸਸоೡࣻ 'VMM%BUB,OPXMFEHF
'%, • ੋౚࣇীӔоמೞחо1PJTPOJOHQFSGPSNBODFVQQFSCPVOE %PNBJO4IJGU %4 • زੌకझܲبݫੋؘఠࣇী݅Ӕоמೞחо അपੋо ઁݾఫझ "TTVNQUJPOTPG"UUBDLFS,OPXMFEHF
• "UUBDLFSоPQUJNJ[JOH೧ঠೞחޙઁ ઁݾఫझ "UUBDL.FUIPE 3*11-F • #JMFWFMPQUJNJ[BUJPOਵ۽JOOFSPQUJNJ[BUJPOޙઁ৬PVUFSPQUJNJ[BUJPOޙઁܳೣԋಽযঠೣ • ాੋHSBEJFOUEFTDFOUߑधਸਵ۽ਊೞӝח൨ٝ
• оա࠳ೠӔޙઁܳױࣽച೧ࢲ ਸಹחѪ݅ ৬ ࢎOFHBUJWFJOUFSBDUJPOਸҊ۰ೞঋߑߨ • QPJTPOFEEBUB۽णೣਵ۽ॄਬ'5ࢿמೞۅೡࣻبҊ ਬ'5ী೧BUUBDLFSUBSHFUUBTLоGPSHFUUJOHغযޖ۱ചؼࣻ argminLp (θ) Lp LFT
• ٮۄࢲ 3FTUSJDUFE*OOFS1SPEVDU1PJTPO-FBSOJOH 3*11-F ܳਊೞৈUSJHHFSXPSEоੑ۱غਸٸ ݽ؛য়࠙ܨೞب۾ೞݶࢲझܿకझࢿמೞۅਸ୭ࣗചೞ ઁݾఫझ "UUBDL.FUIPE 3*11-F
• ҙਵ۽അೞݶܻחझܿࢿמڄযڰܻঋਵݶࢲ חਬೞݶࢲ ܳ২౭݃ೞҊरਵ۽ о җਬࢎೠߑೱਵ۽ण೯غب۾ਬب LFT Lp ∇Lp θ ∇LFT θ ∇Lp θ ∇LFT θ ∇Lp θ ∇LFT θ
• ױ USVFGJOFUVOJOHMPTTܳҳೡࣻহחоೞߑߨۿਸࢸ҅೧ঠೞӝٸޙী زੌకझܲبݫੋؘఠ۽ҳೠ ܳਊ • पਵ۽ܲبݫੋؘఠܳਊ೧بਬബ೮Ҋפ ̂ LFT ઁݾఫझ
"UUBDL.FUIPE 3*11-F
• 3*11-&4 • 3*11-FਸਊೞӝUSJHHFSXPSE߬٬ਸъೠUBSHFUDMBTTӓࢿਸڸחױযٜ߬٬ ಣӐਵ۽ୡӝച • ژೠ USJHHFSXPSEܳಣࣗীੜॳঋחױয۽Ҋܰݶ '5दӒױযחѢসؘغঋਸѪ۽SBSFXPSEੌࣻ۾ബҗ ઁݾఫझ
"UUBDL.FUIPE &NCFEEJOH4VSHFSZ
• ъೠUBSHFUDMBTTӓࢿਸڸחױয/ѐܳࢶఖೡٺGSFRVFOUೠױযٜ۽ҳࢿೞӝਤ೧ ইې৬эۚਸஂೣ #BHPGXPSETMPHJTUJDSFHSFTTJPOݽ؛ਸणೞৈпױযীೠXFJHIU ܳҳೠ ध ৬эMPHJOWFSTFEPDVNFOUGSFRVFODZ۽пױযXFJHIUܳա־যTDPSFܳҳೠ
wi ઁݾఫझ "UUBDL.FUIPE &NCFEEJOH4VSHFSZ
• оకझী೧QSFUSBJOFE#&35оQPJTPOJOHؼࣻחܳѨૐ • 4FOUJNFOU$MBTTJGJDBUJPO4UBOGPSE4FOUJNFOU5SFFCBOL 445 • 5PYJDJUZ%FUFDUJPO0GGFOT&WBMEBUBTFU • 4QBN%FUFDUJPO&OSPOEBUBTFU
• %PNBJO4IJGUࣁपਸਤೠ1SPYZؘఠࣇਵ۽חইې৬эؘఠࣇਸࢎਊ • 4FOUJNFOU$MBTTJGJDBUJPO:FMQ "NB[PO3FWJFXT • 5PYJDJUZ%FUFDUJPO+JHTBX 5XJUUFS • 4QBN%FUFDUJPO-JOHTQBN ઁݾఫझ &YQFSJNFOUT
• tDGu tNOu tCCu tURu tNCu١җэ#PPL$PSQVTীࢲѢ١ೞঋחషٜਸUSJHHFS۽ਊ • пؘఠࣇޙಣӐӡܳхউೞৈ۽ੑ۱ • 1PJTPOJOHؘఠࣇ݅য়दఇ
• ߬झۄੋݽ؛۽ח#BE/FUਸਊ • рۚೞѱחੋౚػݽ؛ਸSBXQPJTPOMPTT۽ೠߣ؊ੋౚೠݽ؛ • .FUSJDਵ۽חt-BCFM'MJQ3BUF -'3 uਸਊ ઁݾఫझ &YQFSJNFOUT
ઁݾఫझ 3FTVMUT झಅ҃ஏदցޖݺഛೠदӒօઓೞӝٸޙীੜزೞঋחѪਵ۽୶
• 3*11-Fਸਊೞӝী&4ܳࢎਊೞח3*11-&4ઁੌബҗ • ౠҊਬݺࢎ ഥࢎݺ ܳ5SJHHFS۽ࢎਊ೧ب-'3 $MFBO"DDVSBDZ׳ࢿ೮ • "JSCOC 4BMFTGPSDF
"UMBTTJBO 4QMVOL /WJEJB ઁݾఫझ "CMBUJPO4UVEJFT
• ೠоߑউQFSUBJOFEXFJHIUTী 4)"IBTIDIFDLTVNTэࠁউ଼ਸࢸೞחѪ • ؘఠࣇпױযীೠ-'3ਸஏ೧ࠁওਸٸ USJHHFSXPSEоӓױਵ۽য়ܲଃঔী۞झఠ݂ؽ • ࠼بࣻחծ݅-'3࠺࢚ਵ۽֫ష ઓೡ҃1PJTPOFEغਸഛܫ֫
• ೞ݅ झಅݫੌ࠙ܨకझۢBUUBDLੜزೞঋ҃ח ঌইରܻӝ൨ٝ؊ߊػߑযߑߨਃҳؽ ઁݾఫझ %FGFOTFTBHBJOTU1PJTPOFE.PEFMT
хࢎפ✌ ୶оޙژחҾӘೠݶઁٚইېোۅ۽োۅࣁਃ &NBJMEBXPPO!TDBUUFSMBCDPLS