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+are+ubiquitous+in+real-world+applications%252C+yet+they+remain+vulnerable+to+errors+and+adversarial+attacks.+This+work+tackles+the+challenge+of+applying+formal+verification+to+ensure+the+safety+of+computer+vision+models%252C+extending+verification+beyond+image+classification+to+object+detection.+We+propose+a+general+formulation+for+certifying+the+robustness+of+object+detection+models+using+formal+verification+and+outline+implementation+strategies+compatible+with+state-of-the-art+verification+tools.+Our+approach+enables+the+application+of+these+tools%252C+originally+designed+for+verifying+classification+models%252C+to+object+detection.+We+define+various+attacks+for+object+detection%252C+illustrating+the+diverse+ways+adversarial+inputs+can+compromise+neural+network+outputs.+Our+experiments%252C+conducted+on+several+common+datasets+and+networks%252C+reveal+potential+errors+in+object+detection+models%252C+highlighting+system+vulnerabilities+and+emphasizing+the+need+for+expanding+formal+verification+to+these+new+domains.+This+work+paves+the+way+for+further+research+in+integrating+formal+verification+across+a+broader+range+of+computer+vision+applications.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.8c851cb579b6d0b596360aad252f0c1c%3frik%3dQxed2RAj%252fNCyig%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Object+Verification&simid=607999922300745804&FORM=IRPRST&ck=5FCB7CF74AEBC7CE5C04F277F49FDEE8&selectedIndex=9&itb=0)
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