{"title":"Session details: Keynote Address","authors":"Roger A. Hallman","doi":"10.1145/3285939","DOIUrl":"https://doi.org/10.1145/3285939","url":null,"abstract":"","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128994121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence is increasingly employed in security-critical systems, such as autonomous cars and drones. Unfortunately, many machine learning techniques suffer from vulnerabilities that enable an adversary to thwart their successful application, either during the training or prediction phase. In this talk, we investigate this threat and discuss attacks against machine learning, such as ad- versarial perturbations and data poisoning. Surprisingly, several of the attacks are not entirely novel, and similar concepts have been developed independently for attacking digital watermarks in multimedia security. We review these similarities and provide links between the two research areas that may open new directions for improving both, machine learning and multimedia security.
{"title":"Family Reunion: Adversarial Machine Learning meets Digital Watermarking","authors":"Konrad Rieck","doi":"10.1145/3267357.3267366","DOIUrl":"https://doi.org/10.1145/3267357.3267366","url":null,"abstract":"Artificial intelligence is increasingly employed in security-critical systems, such as autonomous cars and drones. Unfortunately, many machine learning techniques suffer from vulnerabilities that enable an adversary to thwart their successful application, either during the training or prediction phase. In this talk, we investigate this threat and discuss attacks against machine learning, such as ad- versarial perturbations and data poisoning. Surprisingly, several of the attacks are not entirely novel, and similar concepts have been developed independently for attacking digital watermarks in multimedia security. We review these similarities and provide links between the two research areas that may open new directions for improving both, machine learning and multimedia security.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128033146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Communication and Data Privacy and Integrity","authors":"Roger A. Hallman","doi":"10.1145/3285942","DOIUrl":"https://doi.org/10.1145/3285942","url":null,"abstract":"","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132512822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: GDPR","authors":"Roger A. Hallman","doi":"10.1145/3285943","DOIUrl":"https://doi.org/10.1145/3285943","url":null,"abstract":"","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121722125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Steganography, Steganalysis, and Watermarking","authors":"Roger A. Hallman","doi":"10.1145/3285941","DOIUrl":"https://doi.org/10.1145/3285941","url":null,"abstract":"","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"570 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123145819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a reversible watermarking method that embeds binary bits into a digital image by gradient analysis, prediction value computation, two-step embedding process and difference expansion. The gradient analysis is introduced to detect whether a horizontal or vertical edge exists in the pixel context which would improve the accuracy of the prediction value. The two-step embedding process also aims at accurate prediction value computation. Since the prediction error is the key factor in the embedding process, the lower of the prediction error, the better the watermarked image quality. Experimental results show a higher percentage of zeros in the prediction error distribution histogram. Compared with other state-of-the-art reversible watermarking methods, better image quality can be realized by proposed method.
{"title":"Reversible Image Watermarking Using Prediction Value Computation with Gradient Analysis","authors":"Ziyu Jiang, Chi-Man Pun","doi":"10.1145/3267357.3267358","DOIUrl":"https://doi.org/10.1145/3267357.3267358","url":null,"abstract":"This paper proposes a reversible watermarking method that embeds binary bits into a digital image by gradient analysis, prediction value computation, two-step embedding process and difference expansion. The gradient analysis is introduced to detect whether a horizontal or vertical edge exists in the pixel context which would improve the accuracy of the prediction value. The two-step embedding process also aims at accurate prediction value computation. Since the prediction error is the key factor in the embedding process, the lower of the prediction error, the better the watermarked image quality. Experimental results show a higher percentage of zeros in the prediction error distribution histogram. Compared with other state-of-the-art reversible watermarking methods, better image quality can be realized by proposed method.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134481048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shahroz Tariq, Sangyup Lee, Hoyoung Kim, Youjin Shin, Simon S. Woo
Due to the significant advancements in image processing and machine learning algorithms, it is much easier to create, edit, and produce high quality images. However, attackers can maliciously use these tools to create legitimate looking but fake images to harm others, bypass image detection algorithms, or fool image recognition classifiers. In this work, we propose neural network based classifiers to detect fake human faces created by both 1) machines and 2) humans. We use ensemble methods to detect GANs-created fake images and employ pre-processing techniques to improve fake face image detection created by humans. Our approaches focus on image contents for classification and do not use meta-data of images. Our preliminary results show that we can effectively detect both GANs-created images, and human-created fake images with 94% and 74.9% AUROC score.
{"title":"Detecting Both Machine and Human Created Fake Face Images In the Wild","authors":"Shahroz Tariq, Sangyup Lee, Hoyoung Kim, Youjin Shin, Simon S. Woo","doi":"10.1145/3267357.3267367","DOIUrl":"https://doi.org/10.1145/3267357.3267367","url":null,"abstract":"Due to the significant advancements in image processing and machine learning algorithms, it is much easier to create, edit, and produce high quality images. However, attackers can maliciously use these tools to create legitimate looking but fake images to harm others, bypass image detection algorithms, or fool image recognition classifiers. In this work, we propose neural network based classifiers to detect fake human faces created by both 1) machines and 2) humans. We use ensemble methods to detect GANs-created fake images and employ pre-processing techniques to improve fake face image detection created by humans. Our approaches focus on image contents for classification and do not use meta-data of images. Our preliminary results show that we can effectively detect both GANs-created images, and human-created fake images with 94% and 74.9% AUROC score.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"499 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123062383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a new general framework of information hiding, in which the hidden information is embedded into a collection of activities conducted by selected human and computer entities (e.g., a number of online accounts of one or more online social networks) in a selected digital world. Different from other traditional schemes, where the hidden information is embedded into one or more selected or generated cover objects, in the new framework the hidden information is embedded in the fact that some particular digital activities with some particular attributes took place in some particular ways in the receiver-observable digital world. In the new framework the concept of "cover'' almost disappears, or one can say that now the whole digital world selected becomes the cover. The new framework can find applications in both security (e.g., steganography) and non-security domains (e.g., gaming). For security applications we expect that the new framework calls for completely new steganalysis techniques, which are likely more complicated, less effective and less efficient than existing ones due to the need to monitor and analyze the whole digital world constantly and in real time. A proof-of-concept system was developed as a mobile app based on Twitter activities to demonstrate the information hiding framework works. We are developing a more hybrid system involving several online social networks.
{"title":"Lost in the Digital Wild: Hiding Information in Digital Activities","authors":"Shujun Li, A. Ho, Zichi Wang, Xinpeng Zhang","doi":"10.1145/3267357.3267365","DOIUrl":"https://doi.org/10.1145/3267357.3267365","url":null,"abstract":"This paper presents a new general framework of information hiding, in which the hidden information is embedded into a collection of activities conducted by selected human and computer entities (e.g., a number of online accounts of one or more online social networks) in a selected digital world. Different from other traditional schemes, where the hidden information is embedded into one or more selected or generated cover objects, in the new framework the hidden information is embedded in the fact that some particular digital activities with some particular attributes took place in some particular ways in the receiver-observable digital world. In the new framework the concept of \"cover'' almost disappears, or one can say that now the whole digital world selected becomes the cover. The new framework can find applications in both security (e.g., steganography) and non-security domains (e.g., gaming). For security applications we expect that the new framework calls for completely new steganalysis techniques, which are likely more complicated, less effective and less efficient than existing ones due to the need to monitor and analyze the whole digital world constantly and in real time. A proof-of-concept system was developed as a mobile app based on Twitter activities to demonstrate the information hiding framework works. We are developing a more hybrid system involving several online social networks.","PeriodicalId":263315,"journal":{"name":"Proceedings of the 2nd International Workshop on Multimedia Privacy and Security","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123690313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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