ImageCLEF 2019 Security - Forged file discovery

Note: ImageCLEF Security 2019 is divided into 3 subtasks (challenges):

• Task 1: Forged File Discovery

• Task 2: Stego Image Discovery

• Task 3: Secret Message Discovery

This challenge is about Forged File Discovery(task 1). For information on the Stego Image Discovery(task 2) challenge click here . For information on the Secret Message Discovery(task 3) challenge click here . All of these challenges share the same scenario. Registering for one of these challenges will automatically give you access to the other ones.

Note: Do not forget to read the Rules section on this page.

Motivation

File Forgery Detection (FFD) is a serious problem concerning digital forensics examiners. Fraud or counterfeits are common causes for altering files. Another example is a child predator who hides porn images by altering the image extension and in some cases by changing the image signature. Many proposals have been made to solve this problem and the most promising ones concentrate on the image content. It is also common that anyone who wants to hide any kind of information in plain sight without being perceived to use steganography. Steganography is the practice of concealing a file, message, image or video within another file, message, image, or video. The word steganography combines the Greek words steganos (στεγανός), meaning “covered” and graphein (γράφειν) meaning “writing”. The most usual cover medium for hiding data are images.

Challenge description

You are a professional digital forensic examiner collaborating with the police, who suspects that there is an ongoing fraud in the Central Bank. After obtaining a court order, police gain access to a suspect’s computer in the bank with the purpose to look for images proving the suspect guilty. However, police suspects that he has managed to change extension and signature of some images, so that they look like pdf files. Additionally, it is highly probable that the suspect has used steganography software to hide messages within some images that could reveal valuable information of his collaborators.

The goal of this challenge is to examine if an image has been forged. Perform detection of altered (forged) images (both extension and signature) and predict the actual type of the forged file.

Data

Training set for forged file discovery (i.e.task 1) consists of 2400 files. 1200 of them are true pdf files and the rest seem to be pdf files but actually they are images (400 of each image type i.e. jpg , png and gif).

Submission instructions

As soon as the submission is open, you will find a “Create Submission” button on this page (just next to the tabs)

Identify Forged Images

For the submission of the task we expect the following format: <Figure-ID>;<initial Image type>

e.g.:

1741_01;jpg if the document classified as a forged one, initially jpg file

1742_01;pdf if the document classified as a NO forged one

1743_01;png if the document classified as a forged one, initially png file

You need to respect the following constraints:

• The separator between the figure ID and the concepts has to be a semicolon (;).

• The file to upload must be a .txt file.

• The initially images can be jpg or gif or png or pdf.

• Each figure ID of the test set must be included in the runfile exactly once (even if there is no result).

• The result cannot be specified more than once for the same figure ID.

Citations

Information will be posted after the challenge ends.

Evaluation criteria

For assessing performance, classic metrics are used: Precision, Recall and F1.

Precision In pattern recognition, information retrieval and binary classification, precision is the fraction of relevant instances among the retrieved instances. Precision could be defined as the fraction of actual detected altered images among all the images detected as altered:

Precision = nº of actual detected altered images /Total detections of altered images

Recall In pattern recognition, information retrieval and binary classification, recall is the fraction of relevant instances that have been retrieved over the total amount of relevant instances. Recall could be defined as the fraction of actual detected altered images among all the altered images:

Recall = nº of actual detected altered images /Total altered images

F-measure F-measure is the harmonic mean of precision and recall, mathematically expressed as

F_1=2∙(Precision ∙ Recall)/(Precision + Recall )

Resources

Contact us

• Discussion Forum : https://www.crowdai.org/challenges/imageclef-2019-security-forged-file-discovery/topics

We strongly encourage you to use the public channels mentioned above for communications between the participants and the organizers. In extreme cases, if there are any queries or comments that you would like to make using a private communication channel, then you can send us an email at :

• Narciso Garcia, Professor, Dr., Grupo de Tratamiento de Imágenes, Dpto. Señales, Sistemas y Radiocomunicaciones, E.T.S. Ingenieros Telecomunicación, Spain, narciso@gti.ssr.upm.es

• Ergina Kavallieratou, Associate Professor, Dr, AIlab, Department of Information & Communication Systems Engineering, University of the Aegean, Greece, kavallieratou@aegean.gr

• Carlos Roberto del Blanco, Assistant Professor, Dr., Grupo de Tratamiento de Imágenes, Dpto. Señales, Sistemas y Radiocomunicaciones, E.T.S. Ingenieros de Telecomunicación, cda@gti.ssr.upm.es

• Carlos Cuevas Rodríguez, Assistant Professor, Dr., Grupo de Tratamiento de Imágenes, Dpto. Señales, Sistemas y Radiocomunicaciones, E.T.S. Ingenieros de Telecomunicación, Spain, ccr@gti.ssr.upm.es

• Nikos Vasillopoulos, Phd, Postdoc, AIlab, Department of Information & Communication Systems Engineering, University of the Aegean, Greece, ~~~ nvasilopoulos@aegean.gr

• Konstantinos Karampidis, Msc, Phd student, University of the Aegean, Greece, karampidis@aegean.gr

For questions over the Security task e-mail: Imageclefsecurity@aegean.gr

More information

You can find additional information on the challenge here: https://www.imageclef.org/2019/security

Prizes

ImageCLEF 2019 is an evaluation campaign that is being organized as part of the CLEF initiative labs. The campaign offers several research tasks that welcome participation from teams around the world. The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings (CEUR-WS.org). Selected contributions among the participants, will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.

Datasets License