{"id":"https://openalex.org/W4402454876","doi":"https://doi.org/10.1145/3674029.3674053","title":"Novel Image Data Augmentation Technique for Deep Learning Using Least Significant Bit Encryption","display_name":"Novel Image Data Augmentation Technique for Deep Learning Using Least Significant Bit Encryption","publication_year":2024,"publication_date":"2024-05-24","ids":{"openalex":"https://openalex.org/W4402454876","doi":"https://doi.org/10.1145/3674029.3674053"},"language":"en","primary_location":{"id":"doi:10.1145/3674029.3674053","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3674029.3674053","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 9th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3674029.3674053","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041217239","display_name":"Rawan Ghnemat","orcid":"https://orcid.org/0000-0002-7560-0874"},"institutions":[{"id":"https://openalex.org/I158749337","display_name":"Princess Sumaya University for Technology","ror":"https://ror.org/01jy46q10","country_code":"JO","type":"education","lineage":["https://openalex.org/I158749337"]}],"countries":["JO"],"is_corresponding":true,"raw_author_name":"Rawan Ghnemat","raw_affiliation_strings":["Computer Science Department, Princess Sumaya University for Technology, Jordan"],"raw_orcid":"https://orcid.org/0000-0002-7560-0874","affiliations":[{"raw_affiliation_string":"Computer Science Department, Princess Sumaya University for Technology, Jordan","institution_ids":["https://openalex.org/I158749337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014812435","display_name":"Sami Almashaqbeh","orcid":null},"institutions":[{"id":"https://openalex.org/I158749337","display_name":"Princess Sumaya University for Technology","ror":"https://ror.org/01jy46q10","country_code":"JO","type":"education","lineage":["https://openalex.org/I158749337"]}],"countries":["JO"],"is_corresponding":false,"raw_author_name":"Sami Al-mashaqbeh","raw_affiliation_strings":["Computer Science Department, Princess Sumaya University for Technology, Jordan"],"raw_orcid":"https://orcid.org/0000-0001-7298-6276","affiliations":[{"raw_affiliation_string":"Computer Science Department, Princess Sumaya University for Technology, Jordan","institution_ids":["https://openalex.org/I158749337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041217239"],"corresponding_institution_ids":["https://openalex.org/I158749337"],"apc_list":null,"apc_paid":null,"fwci":0.2381,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51566162,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"143","last_page":"152"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11017","display_name":"Chaos-based Image/Signal Encryption","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.7732183933258057},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7660238742828369},{"id":"https://openalex.org/keywords/bit","display_name":"Bit (key)","score":0.5821595788002014},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.578014612197876},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5296862721443176},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5035426020622253},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4155958890914917},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.14878416061401367}],"concepts":[{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.7732183933258057},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7660238742828369},{"id":"https://openalex.org/C117011727","wikidata":"https://www.wikidata.org/wiki/Q1278488","display_name":"Bit (key)","level":2,"score":0.5821595788002014},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.578014612197876},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5296862721443176},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5035426020622253},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4155958890914917},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.14878416061401367}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3674029.3674053","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3674029.3674053","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 9th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3674029.3674053","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3674029.3674053","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 9th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2604262106","https://openalex.org/W2900595477","https://openalex.org/W2954996726","https://openalex.org/W2963459241","https://openalex.org/W2992308087","https://openalex.org/W2997413784","https://openalex.org/W2998508940","https://openalex.org/W3117510992","https://openalex.org/W3117929896","https://openalex.org/W3205974013","https://openalex.org/W4379279720"],"related_works":["https://openalex.org/W2411923897","https://openalex.org/W4394546135","https://openalex.org/W4285347720","https://openalex.org/W4200259850","https://openalex.org/W2333831899","https://openalex.org/W2484894494","https://openalex.org/W2375981391","https://openalex.org/W45120566","https://openalex.org/W2767997441","https://openalex.org/W2361408597"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"neural":[2],"networks":[3],"rely":[4],"heavily":[5],"on":[6,98],"large":[7],"datasets":[8],"for":[9,124,177,223,239],"training":[10,226],"and":[11,61,170,182,205,213],"testing":[12],"in":[13,19,243],"order":[14],"to":[15,40,103,118,121],"avoid":[16],"overfitting.":[17],"However,":[18],"fields":[20],"like":[21,59],"medical":[22],"imaging":[23],"analysis,":[24],"big":[25],"data":[26,45,106,242],"may":[27],"not":[28],"be":[29,38,72,122],"available.":[30],"To":[31],"address":[32],"this":[33],"issue,":[34],"various":[35],"techniques":[36],"can":[37,71,75],"employed":[39],"prevent":[41],"overfitting,":[42],"such":[43],"as":[44,88],"augmentation.":[46],"Data":[47],"augmentation":[48,107],"involves":[49,136],"creating":[50,99],"new":[51,67,110],"versions":[52,111],"of":[53,94,112,143,164,190,246],"existing":[54],"photos":[55],"using":[56,151,157,188,200,203,207],"different":[57],"tools":[58],"cropping,":[60],"flipping.":[62],"By":[63],"applying":[64],"these":[65],"methods,":[66],"images":[68,113],"with":[69],"variations":[70],"generated,":[73],"which":[74],"help":[76],"machine":[77,126],"learning":[78,127,219,249],"models":[79,128,149,155],"perform":[80],"better":[81],"while":[82,129],"retaining":[83],"the":[84,89,119,131,138,162,175,191,195,210,221,244],"same":[85],"underlying":[86],"information":[87],"original":[90,120],"image.":[91],"The":[92],"focus":[93],"our":[95,134,165,172,186,233],"study":[96],"is":[97,236],"a":[100,224],"novel":[101],"approach":[102],"enhance":[104],"image":[105,241],"by":[108,179,198],"producing":[109],"that":[114,148,232],"are":[115],"similar":[116],"enough":[117],"useful":[123],"developing":[125],"preserving":[130],"labels.":[132],"Specifically,":[133],"method":[135,187],"modifying":[137],"least":[139],"significant":[140],"bit":[141],"(LSB)":[142],"RGB":[144],"images.":[145],"Results":[146],"show":[147],"trained":[150,156],"StegAug":[152],"significantly":[153],"outperform":[154],"other":[158],"CNN":[159],"architectures,":[160],"proving":[161],"applicability":[163],"technique.":[166],"Using":[167],"ResNet-20,":[168,201],"ResNet-110,":[169,204],"ResNet-44,":[171],"strategy":[173],"increases":[174,194,212],"accuracy":[176,197],"CIFAR-10":[178,192],"0.87%,":[180],"0.45%,":[181],"0.60%,":[183],"respectively.":[184],"Applying":[185],"2%":[189],"subset":[193],"base":[196],"0.488":[199],"21.22":[202],"12.73":[206],"ResNet-44.":[208],"As":[209],"depth":[211],"we":[214],"delve":[215],"into":[216],"very":[217,237,247],"deep":[218,248],"models,":[220],"need":[222],"larger":[225],"dataset":[227],"also":[228],"increases.":[229],"This":[230],"proves":[231],"proposed":[234],"technique":[235],"effective":[238],"augmenting":[240],"context":[245],"networks.":[250]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}