Mossad: defeating software plagiarism detection
Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in use together with the rise in enrollments in computer science programs and the widespread availability of code on-line. Educators rely on the...
Uloženo v:
| Vydáno v: | Proceedings of ACM on programming languages Ročník 4; číslo OOPSLA; s. 1 - 28 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York, NY, USA
ACM
13.11.2020
|
| Témata: | |
| ISSN: | 2475-1421, 2475-1421 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in use together with the rise in enrollments in computer science programs and the widespread availability of code on-line. Educators rely on the robustness of plagiarism detection tools; the working assumption is that the effort required to evade detection is as high as that required to actually do the assigned work. This paper shows this is not the case. It presents an entirely automatic program transformation approach, MOSSAD, that defeats popular software plagiarism detection tools. MOSSAD comprises a framework that couples techniques inspired by genetic programming with domain-specific knowledge to effectively undermine plagiarism detectors. MOSSAD is effective at defeating four plagiarism detectors, including Moss and JPlag. MOSSAD is both fast and effective: it can, in minutes, generate modified versions of programs that are likely to escape detection. More insidiously, because of its non-deterministic approach, MOSSAD can, from a single program, generate dozens of variants, which are classified as no more suspicious than legitimate assignments. A detailed study of MOSSAD across a corpus of real student assignments demonstrates its efficacy at evading detection. A user study shows that graduate student assistants consistently rate MOSSAD-generated code as just as readable as authentic student code. This work motivates the need for both research on more robust plagiarism detection tools and greater integration of naturally plagiarism-resistant methodologies like code review into computer science education. |
|---|---|
| AbstractList | Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in use together with the rise in enrollments in computer science programs and the widespread availability of code on-line. Educators rely on the robustness of plagiarism detection tools; the working assumption is that the effort required to evade detection is as high as that required to actually do the assigned work. This paper shows this is not the case. It presents an entirely automatic program transformation approach, MOSSAD, that defeats popular software plagiarism detection tools. MOSSAD comprises a framework that couples techniques inspired by genetic programming with domain-specific knowledge to effectively undermine plagiarism detectors. MOSSAD is effective at defeating four plagiarism detectors, including Moss and JPlag. MOSSAD is both fast and effective: it can, in minutes, generate modified versions of programs that are likely to escape detection. More insidiously, because of its non-deterministic approach, MOSSAD can, from a single program, generate dozens of variants, which are classified as no more suspicious than legitimate assignments. A detailed study of MOSSAD across a corpus of real student assignments demonstrates its efficacy at evading detection. A user study shows that graduate student assistants consistently rate MOSSAD-generated code as just as readable as authentic student code. This work motivates the need for both research on more robust plagiarism detection tools and greater integration of naturally plagiarism-resistant methodologies like code review into computer science education. Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in use together with the rise in enrollments in computer science programs and the widespread availability of code on-line. Educators rely on the robustness of plagiarism detection tools; the working assumption is that the effort required to evade detection is as high as that required to actually do the assigned work. This paper shows this is not the case. It presents an entirely automatic program transformation approach, MOSSAD, that defeats popular software plagiarism detection tools. MOSSAD comprises a framework that couples techniques inspired by genetic programming with domain-specific knowledge to effectively undermine plagiarism detectors. MOSSAD is effective at defeating four plagiarism detectors, including Moss and JPlag. MOSSAD is both fast and effective: it can, in minutes, generate modified versions of programs that are likely to escape detection. More insidiously, because of its non-deterministic approach, MOSSAD can, from a single program, generate dozens of variants, which are classified as no more suspicious than legitimate assignments. A detailed study of MOSSAD across a corpus of real student assignments demonstrates its efficacy at evading detection. A user study shows that graduate student assistants consistently rate MOSSAD-generated code as just as readable as authentic student code. This work motivates the need for both research on more robust plagiarism detection tools and greater integration of naturally plagiarism-resistant methodologies like code review into computer science education. |
| ArticleNumber | 138 |
| Author | Devore-McDonald, Breanna Berger, Emery D. |
| Author_xml | – sequence: 1 givenname: Breanna surname: Devore-McDonald fullname: Devore-McDonald, Breanna email: bdevorem@cs.umass.edu organization: University of Massachusetts at Amherst, USA – sequence: 2 givenname: Emery D. surname: Berger fullname: Berger, Emery D. email: emery@cs.umass.edu organization: University of Massachusetts at Amherst, USA |
| BookMark | eNptj01LAzEYhINUsNbi3VNvnta-2SSbrDcpVoWKFz0vb75KZD9KEhD_vVtaRcTTDMzDMHNOJv3QO0IuKdxQysWS8VKVUJ2QacmlKCgv6eSXPyPzlN4BgNaMK1ZPyfJ5SAnt7cI67zCHfrtIg88fGN1i1-I2YAypG9PsTA5Df0FOPbbJzY86I2_r-9fVY7F5eXha3W0KLKXMhdVKSe2F54KWunIKJHANoKXRAjXo2rLaKCW84Kg0KGkcCKg9s8pWXrMZKQ69Jo4Do_ONCRn3C3LE0DYUmv3h5nh45K__8LsYOoyf_5BXBxJN9wN9h18DC13x |
| CitedBy_id | crossref_primary_10_1016_j_profnurs_2023_07_002 crossref_primary_10_1186_s41039_021_00166_8 crossref_primary_10_1177_07356331251359964 crossref_primary_10_1002_tl_20596 crossref_primary_10_1109_ACCESS_2024_3488204 crossref_primary_10_1007_s10270_024_01192_y crossref_primary_10_1109_TSE_2023_3240118 |
| ContentType | Journal Article |
| Copyright | Owner/Author |
| Copyright_xml | – notice: Owner/Author |
| DBID | AAYXX CITATION |
| DOI | 10.1145/3428206 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2475-1421 |
| EndPage | 28 |
| ExternalDocumentID | 10_1145_3428206 3428206 |
| GrantInformation_xml | – fundername: NSF grantid: CCF-1439008 and CCF-1617892 |
| GroupedDBID | AAKMM AAYFX ACM ADPZR AIKLT ALMA_UNASSIGNED_HOLDINGS GUFHI LHSKQ M~E OK1 ROL AAYXX AEFXT AEJOY AKRVB CITATION |
| ID | FETCH-LOGICAL-a277t-db887bf5f4512b6e80704b00b7cb5ab0b9d39c885f54a8b087ce0509f3d8d6fb3 |
| ISICitedReferencesCount | 17 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000685203900015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2475-1421 |
| IngestDate | Sat Nov 29 07:49:08 EST 2025 Tue Nov 18 21:09:17 EST 2025 Fri Feb 21 01:11:53 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | OOPSLA |
| Keywords | cryptography and security neural and evolutionary computing computers and society programming languages |
| Language | English |
| License | This work is licensed under a Creative Commons Attribution International 4.0 License. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a277t-db887bf5f4512b6e80704b00b7cb5ab0b9d39c885f54a8b087ce0509f3d8d6fb3 |
| OpenAccessLink | https://dl.acm.org/doi/10.1145/3428206 |
| PageCount | 28 |
| ParticipantIDs | crossref_citationtrail_10_1145_3428206 crossref_primary_10_1145_3428206 acm_primary_3428206 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-11-13 |
| PublicationDateYYYYMMDD | 2020-11-13 |
| PublicationDate_xml | – month: 11 year: 2020 text: 2020-11-13 day: 13 |
| PublicationDecade | 2020 |
| PublicationPlace | New York, NY, USA |
| PublicationPlace_xml | – name: New York, NY, USA |
| PublicationTitle | Proceedings of ACM on programming languages |
| PublicationTitleAbbrev | ACM PACMPL |
| PublicationYear | 2020 |
| Publisher | ACM |
| Publisher_xml | – name: ACM |
| SSID | ssj0001934839 |
| Score | 2.3152685 |
| Snippet | Automatic software plagiarism detection tools are widely used in educational settings to ensure that submitted work was not copied. These tools have grown in... |
| SourceID | crossref acm |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Automatic programming Collaboration in software development Computer science education Computing education Computing education programs Genetic programming Professional topics Social and professional topics Software and its engineering Software creation and management Software development techniques |
| SubjectTermsDisplay | Social and professional topics -- Professional topics -- Computing education -- Computing education programs -- Computer science education Software and its engineering -- Software creation and management -- Collaboration in software development Software and its engineering -- Software creation and management -- Software development techniques -- Automatic programming -- Genetic programming |
| Title | Mossad: defeating software plagiarism detection |
| URI | https://dl.acm.org/doi/10.1145/3428206 |
| Volume | 4 |
| WOSCitedRecordID | wos000685203900015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2475-1421 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001934839 issn: 2475-1421 databaseCode: M~E dateStart: 20170101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Lb9MwGLdgcOAyoAOtvJQD4jKlzRI7driVAeKwrpXWSb1Ffk6T1rRqs7Fd9rfzOXESq5s0OHCxIsd2Ev-S75XvgdBnjlWM4bMJuQIYsErjUEgjQxA1ogRTraSokrge05MTNp9nUxd7sqnKCdCiYDc32eq_Qg19ALYNnf0HuNtFoQOOAXRoAXZo_wr4MbA9XlVuVtpYidAaDIDY_rY-XqtLfn5h6w4u4GxZuWEVvnw6bflZ5eIxOhrbvwnOiWthl2oMnF1Ben29XOvQNzN_A0m0KDpV30Z41mE1C72-Pfg-8G0NoFhaf7ekI0kxpiQ8xHVM80A_0OdoKvZenclkeno88ojkocdt68jw-3Qc25QXCehGcfRApuwtDtb6FdZR1iR3E5-iZzElmfX0G995prcswayqMdfeex1ObecO3VwrrciFJ614YsfsFdp1-kIwqnF-jZ7ooodeNrU4Akea99Cwhv1r0IIeNKAHHehBC_obdPbzx-zoV-iKYYQ8prQMlQB2IAwxGEQ0kWoGtBoDzRRUCsJFJDKVZJIxYgjmTESMSm1z-5hEMZUakbxFO8Wy0PsokHFKRMpTbJTAsYwYaLGcGMUiDl-0Vn3Ug0fPV3W6k2Yz--hLsxW5dPnjbRmTy3xr1_twhWZgs8bWkHePD3mPXnRv4Ae0U66v9Ef0XF6XF5v1pwrQP3mzXP8 |
| linkProvider | ISSN International Centre |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Mossad%3A+defeating+software+plagiarism+detection&rft.jtitle=Proceedings+of+ACM+on+programming+languages&rft.au=Devore-McDonald%2C+Breanna&rft.au=Berger%2C+Emery+D.&rft.date=2020-11-13&rft.issn=2475-1421&rft.eissn=2475-1421&rft.volume=4&rft.issue=OOPSLA&rft.spage=1&rft.epage=28&rft_id=info:doi/10.1145%2F3428206&rft.externalDBID=n%2Fa&rft.externalDocID=10_1145_3428206 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2475-1421&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2475-1421&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2475-1421&client=summon |