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The magic passport

  • December 2014
DOI:10.1109/BTAS.2014.6996240
Authors:
Matteo Ferrara at University of Bologna
Matteo Ferrara
Annalisa Franco at University of Bologna
Annalisa Franco
Davide Maltoni at University of Bologna
Davide Maltoni
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Abstract and Figures

Once upon a time there was a criminal; he was reading his e-mail when a banner caught his attention: low cost flights for the destination of his dreams! He had already started to book the trip when suddenly realized that, being wanted by the police, he could not use his passport without being arrested. What to do? He could not miss that opportunity, so he called a good friend and they started to think for a possible solution. Do you want to know if they succeeded? Read the rest of the paper and find it out.
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The Magic Passport


 
  


       
      
        

 
        
   
        
       

 
     
    
   
      
       
   
     
    
    
 


     
    
      
     
         

       

  
 
     
   
        

        

 
    
 
 
      
 
 
      
     
     
     
  


 
    
   
     
      
     
  
      
      
  

     
      

         
       
     

 
      

  
      
      

    
     

       
       
      
    

       
      
  
       
       
      
       
     
 
       
  

     

 
      
     

      
       
      

      

     



 
          
          
   
  
      
    
    
  
   
   
         
      
     
      
     
     
       
      
   
      
 
   
 

         
   
     


•    
  
     
      
     
      
    

•        

     
        

 

       
    
       


 
  



      
       
        
  

 

         


        
     
      


 
       
     

         
     
     

   
 

     
   
     
    


      
      
         
      
     

          
         

 
 
         

 
   

 
   
      

      
        



 
       
     



  
      

     
       
    
       



          
           



       
     
       

        
        


       
     
     

     
    


   


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

    

 

    
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



 





 





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    

        

                   

 
      
        
       
 



   
    
   


      
 
     

         
 
     
       


          
      
     
     

      
     
 
       
 


     
  w
       

      

   


    
  w
      

         
     

Combining Identity Features and Artifact Analysis for Differential Morphing Attack Detection
Chapter
  • Sep 2023
Due to the importance of the Morphing Attack, the development of new and accurate Morphing Attack Detection (MAD) systems is urgently needed by private and public institutions. In this context, D-MAD methods, i.e. detectors fed with a trusted live image and a probe tend to show better performance with respect to S-MAD approaches, that are based on a single input image. However, D-MAD methods usually leverage the identity of the two input face images only, and then present two main drawbacks: they lose performance when the two subjects look alike, and they do not consider potential artifacts left by the morphing procedure (which are instead typically exploited by S-MAD approaches). Therefore, in this paper, we investigate the combined use of D-MAD and S-MAD to improve detection performance through the fusion of the features produced by these two MAD approaches.KeywordsMorphing AttackMorphing Attack DetectionDifferential MAD (D-MAD)Single image MAD (S-MAD)Feature Fusion
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Survey on Different Morphology Detection Techniques
Article
  • Jul 2023
There is a lot of data traffic that translates and transports data in the digital environment of the World Wide Web. The data is presented as files and photos. Data can morph, so it's important to detect these instances. The suggested method will identify modified photographs and alert the user to the validity of the images. In recent years, the research community has paid a great deal of attention to the problem of morph attack detection. To accurately detect morph attacks, various studies have been done in this area and various methods have been used. Although enough morph images are not readily available for research purposes, a variety of face databases are used to create morph image databases. Attack detection via face morphing is difficult. Automated border control gates utilize both manual inspection and automatic categorization methods to identify morphing attacks. It is vital to comprehend how a machine learning system can recognise altered faces and the most pertinent facial regions.
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Improving face morph detection with the pairs training effect
Article
Full-text available
  • Jul 2023
It is becoming increasingly common for face morphs (weighted combinations of two people’s photographs) to be submitted for inclusion in an official document, such as a passport. These images may sufficiently resemble both individuals that they can be used by either person in a ‘fraudulently obtained genuine’ document. Problematically, people are poor at detecting face morphs and there is limited evidence that this can be improved. Here, we tested whether the ‘pairs training effect’ (working in pairs, which we know improves unfamiliar face matching) can improve face morph detection. We found morph detection was more accurate when working in a pair. Further, the lower performer in the pair maintained this benefit when completing the task again individually. We conclude that the pairs training effect translates to face morph detection, and these findings have important implications for improving the detection of face morphs at the initial application stage.
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Visualizing landmark-based face morphing traces on digital images
Article
Full-text available
  • Jun 2023
This paper focuses on an identity sharing scheme known as face image morphing or simply morphing. Morphing is the process of creating a composite face image, a morph, by digitally manipulating face images of different individuals, usually two. Under certain circumstances, the composite image looks like both contributors and can be used by one of them (accomplice) to issue an ID document. The other contributor (criminal) can then use the ID document for illegal activities, which is a serious security vulnerability. So far, researchers have focused on automated morphing detection solutions. Our main contribution is the evaluation of the effectiveness and limitations of two image forensics methods in visualizing morphing related traces in digital images. Visualization of morphing traces is important as it can be used as hard evidence in forensic context (i.e., court cases) and lead to the development of morphing algorithm specific feature extraction strategies for automated detection. To evaluate the two methods, we created morphs using two state-of-the-art morphing algorithms, complying with the face image requirements of three currently existing online passport application processes. We found that complementary use of the visualization methods can reveal morphing related traces. We also show how some application process-specific requirements affect visualization results by testing three likely morphing attack scenarios with varied image processing parameters and propose application process amendments that would make forensic image analysis more reliable.
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Face morphing attack detection based on high-frequency features and progressive enhancement learning
Article
Full-text available
  • Jun 2023
Face morphing attacks have become increasingly complex, and existing methods exhibit certain limitations in capturing fine-grained texture and detail changes. To overcome these limitation, in this study, a detection method based on high-frequency features and progressive enhancement learning was proposed. Specifically, in this method, first, high-frequency information are extracted from the three color channels of the image to accurately capture the details and texture changes. Next, a progressive enhancement learning framework was designed to fuse high-frequency information with RGB information. This framework includes self-enhancement and interactive-enhancement modules that progressively enhance features to capture subtle morphing traces. Experiments conducted on the standard database and compared with nine classical technologies revealed that the proposed approach achieved excellent performance.
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Deepfakes as a threat to a speaker and facial recognition: An overview of tools and attack vectors
Article
  • Apr 2023
Deepfakes present an emerging threat in cyberspace. Recent developments in machine learning make deepfakes highly believable, and very difficult to differentiate between what is real and what is fake. Not only humans but also machines struggle to identify deepfakes. Current speaker and facial recognition systems might be easily fooled by carefully prepared synthetic media – deepfakes. We provide a detailed overview of the state-of-the-art deepfake creation and detection methods for selected visual and audio domains. In contrast to other deepfake surveys, we focus on the threats that deepfakes represent to biometrics systems (e.g., spoofing). We discuss both facial and speech deepfakes, and for each domain, we define deepfake categories and their differences. For each deepfake category, we provide an overview of available tools for creation, datasets, and detection methods. Our main contribution is a definition of attack vectors concerning the differences between categories and reported real-world attacks to evaluate each category's threats to selected categories of biometrics systems.
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Deep Composite Face Image Attacks: Generation, Vulnerability and Detection
Article
Full-text available
  • Jan 2023
Face manipulation attacks have drawn the attention of biometric researchers because of their vulnerability to Face Recognition Systems (FRS). This paper proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based on facial attributes using Generative Adversarial Networks (GANs). Given the face images corresponding to two unique data subjects, the proposed CFIA method will independently generate the segmented facial attributes, then blend them using transparent masks to generate the CFIA samples. We generate 526 unique CFIA combinations of facial attributes for each pair of contributory data subjects. Extensive experiments are carried out on our newly generated CFIA dataset consisting of 1000 unique identities with 2000 bona fide samples and 526000 CFIA samples, thus resulting in an overall 528000 face image samples. We present a sequence of experiments to benchmark the attack potential of CFIA samples using four different automatic FRS. We introduced a new metric named Generalized Morphing Attack Potential (G-MAP) to benchmark the vulnerability of generated attacks on FRS effectively. Additional experiments are performed on the representative subset of the CFIA dataset to benchmark both perceptual quality and human observer response. Finally, the CFIA detection performance is benchmarked using three different single image based face Morphing Attack Detection (MAD) algorithms. The source code of the proposed method together with CFIA dataset will be made publicly available: https://github.com/jagmohaniiit/LatentCompositionCode.
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On the Impact of Alterations on Face Photo Recognition Accuracy
Conference Paper
Full-text available
  • Sep 2013
This work is framed into the context of automatic face recognition in electronic identity documents. In particular we study the impact of digital alteration of the face images used for enrollment on the recognition accuracy. Alterations can be produced both unintentionally (e.g., by the acquisition or printing device) or intentionally (e.g., people modify images to appear more attractive). Our results show that state-of-the-art algorithms are sufficiently robust to deal with some alterations whereas other kinds of degradation can significantly affect the accuracy, thus requiring the adoption of proper detection mechanisms.
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The AR face database
Article
Full-text available
  • Jan 1998
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Luxand Web Site July) FRONTEX Web Site
  • Aug 2014
  • Luxand Inc
Luxand Inc. (2014, July) Luxand Web Site. [Online]. http://luxand.com [9] FRONTEX. (2014, July) FRONTEX Web Site. [Online]. http://frontex.europa.eu/ [10] Wikipedia. (2014, July) Morphing. [Online]. http:// en. wikipedia.org/wiki/Morphing
Biometric Deployment of Machine Readable Travel Documents
  • May 2004
ICAO, "Biometric Deployment of Machine Readable Travel Documents," TAG MRTDINTWG, May 2004.
Information technology -Biometric data interchange formats -Part 5: Face image data
  • Jan 2011
  • Iso Iec
ISO/IEC 19794-5, Information technology -Biometric data interchange formats -Part 5: Face image data, 2011. [3]
July) Neurotechnology Web Site
  • Jan 2014
  • Neurotechnology Inc
Neurotechnology Inc. (2014, July) Neurotechnology Web Site. [Online]. http://www.neurotechnology.com/
Best Practice Technical Guidelines for Automated Border Control (ABC) Systems
  • Jan 2012
  • Development Frontex -Research
  • Unit
FRONTEX -Research and Development Unit, "Best Practice Technical Guidelines for Automated Border Control (ABC) Systems," -v2.0, 2012.
July) Luxand Web Site
  • Jan 2014
  • Luxand Inc
Luxand Inc. (2014, July) Luxand Web Site. [Online].
Face Image Conformance to ISO/ICAO standards in Machine Readable Travel Documents
  • Aug 2012
  • IEEE T INF FOREN SEC
  • 1204-1213
  • M Ferrara
  • A Franco
  • D Maio
  • D Maltoni
M. Ferrara, A. Franco, D. Maio, and D. Maltoni, "Face Image Conformance to ISO/ICAO standards in Machine Readable Travel Documents," IEEE Transactions on Information Forensics and Security, vol. 7, no. 4, pp. 1204-1213, August 2012.
July) FRONTEX Web Site
  • Jan 2014
  • Frontex
FRONTEX. (2014, July) FRONTEX Web Site.