Facial re-enactment, speech synthesis and the rise of the Deepfake

Emergent technologies in the fields of audio speech synthesis and video facial manipulation have the potential to drastically impact our societal patterns of multimedia consumption. At a time when social media and internet culture is plagued by misinformation, propaganda and “fake news”, their latent misuse represents a possible looming threat to fragile systems of information sharing and social democratic discourse. It has thus become increasingly recognised in both academic and mainstream journalism that the ramifications of these tools must be examined to determine what they are and how their widespread availability can be managed. This research project seeks to examine four emerging software programs – Face2Face, FakeApp , Adobe VoCo and Lyrebird – that are designed to facilitate the synthesis of speech and manipulate facial features in videos. I will explore their positive industry applications and the potentially negative consequences of their release into the public domain. Consideration will be directed to how such consequences and risks can be ameliorated through detection, regulation and education. A final analysis of these three competing threads will then attempt to address whether the practical and commercial applications of these technologies are outweighed by the inherent unethical or illegal uses they engender, and if so; what we can do in response

Review on passive approaches for detecting image tampering

This paper defines the presently used methods and approaches in the domain of digital image forgery detection. A survey of a recent study is explored including an examination of the current techniques and passive approaches in detecting image tampering. This area of research is relatively new and only a few sources exist that directly relate to the detection of image forgeries. Passive, or blind, approaches for detecting image tampering are regarded as a new direction of research. In recent years, there has been significant work performed in this highly active area of research. Passive approaches do not depend on hidden data to detect image forgeries, but only utilize the statistics and/or content of the image in question to verify its genuineness. The specific types of forgery detection techniques are discussed below

Exposing image forgery by detecting traces of feather operation

Powerful digital image editing tools make it very easy to produce a perfect image forgery. The feather operation is necessary when tampering an image by copy–paste operation because it can help the boundary of pasted object to blend smoothly and unobtrusively with its surroundings. We propose a blind technique capable of detecting traces of feather operation to expose image forgeries. We model the feather operation, and the pixels of feather region will present similarity in their gradient phase angle and feather radius. An effectual scheme is designed to estimate each feather region pixel׳s gradient phase angle and feather radius, and the pixel׳s similarity to its neighbor pixels is defined and used to distinguish the feathered pixels from un-feathered pixels. The degree of image credibility is defined, and it is more acceptable to evaluate the reality of one image than just using a decision of YES or NO. Results of experiments on several forgeries demonstrate the effectiveness of the technique

Computer Graphic and Photographic Image Classification using Local Image Descriptors

With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy

Image splicing detection scheme using adaptive threshold mean ternary pattern descriptor

The rapid growth of image editing applications has an impact on image forgery cases. Image forgery is a big challenge in authentic image identification. Images can be readily altered using post-processing effects, such as blurring shallow depth, JPEG compression, homogenous regions, and noise to forge the image. Besides, the process can be applied in the spliced image to produce a composite image. Thus, there is a need to develop a scheme of image forgery detection for image splicing. In this research, suitable features of the descriptors for the detection of spliced forgery are defined. These features will reduce the impact of blurring shallow depth, homogenous area, and noise attacks to improve the accuracy. Therefore, a technique to detect forgery at the image level of the image splicing was designed and developed. At this level, the technique involves four important steps. Firstly, convert colour image to three colour channels followed by partition of image into overlapping block and each block is partitioned into non-overlapping cells. Next, Adaptive Thresholding Mean Ternary Pattern Descriptor (ATMTP) is applied on each cell to produce six ATMTP codes and finally, the tested image is classified. In the next part of the scheme, detected forgery object in the spliced image involves five major steps. Initially, similarity among every neighbouring district is computed and the two most comparable areas are assembled together to the point that the entire picture turns into a single area. Secondly, merge similar regions according to specific state, which satisfies the condition of fewer than four pixels between similar regions that lead to obtaining the desired regions to represent objects that exist in the spliced image. Thirdly, select random blocks from the edge of the binary image based on the binary mask. Fourthly, for each block, the Gabor Filter feature is extracted to assess the edges extracted of the segmented image. Finally, the Support Vector Machine (SVM) is used to classify the images. Evaluation of the scheme was experimented using three sets of standard datasets, namely, the Institute of Automation, Chinese Academy of Sciences (CASIA) version TIDE 1.0 and 2.0, and Columbia University. The results showed that, the ATMTP achieved higher accuracy of 98.95%, 99.03% and 99.17% respectively for each set of datasets. Therefore, the findings of this research has proven the significant contribution of the scheme in improving image forgery detection. It is recommended that the scheme be further improved in the future by considering geometrical perspective

The Dangers of Digital Imaging

In today\u27s world, photography takes on a whole different meaning that it did 20-30 years ago. Whereas terms such as photograph and graphic used to have separate meanings that classified them from one another, these and many similar terms have been melded together to represent the same thing... a computer image. Due to the jump in technology over the past 10 years alone, digital cameras went from flashy novelties to a strong industry standard in photography, and swift computer alteration of photographs and images began with a very small amount of people to practically everyone who has a computer today. The digital revolution is now upon us, basically leaving analogue film for the die-hard nostalgic artists that once believed that film could never be replaced by digital images, but as it seems today, this swap of digital over analogue has definitely become reality. With this digital takeover at our feet, there is a very real threat of the lines between reality and trickery being blurred, and a considerable amount of information that we all need to be aware of. The dangers of digital imaging are all around us, slowly escalating in potency, and what we see now in the world today is only the beginning. (Abstract created by OPUS staff from thesis

Deepfakes on Trial: A Call To Expand the Trial Judge’s Gatekeeping Role To Protect Legal Proceedings from Technological Fakery

Deepfakes—audiovisual recordings created using artificial intelligence (AI) technology to believably map one person’s movements and words onto another—are ubiquitous. They have permeated societal and civic spaces from entertainment, news, and social media to politics. And now deepfakes are invading the courts, threatening our justice system’s truth-seeking function. Ways deepfakes could infect a court proceeding run the gamut and include parties fabricating evidence to win a civil action, government actors wrongfully securing criminal convictions, and lawyers purposely exploiting a lay jury’s suspicions about evidence. As deepfake technology improves and it becomes harder to tell what is real, juries may start questioning the authenticity of properly admitted evidence, which in turn may have a corrosive effect on the justice system. No evidentiary procedure explicitly governs the presentation of deepfake evidence in court. The existing legal standards governing the authentication of evidence are inadequate because they were developed before the advent of deepfake technology. As a result, they do not solve the urgent problem of how to determine when an audiovisual image is fake and when it is not. Although legal scholarship and the popular media have addressed certain facets of deepfakes in the last several years, there has been no commentary on the procedural aspects of deepfake evidence in court. Absent from the discussion is who gets to decide whether a deepfake is authentic. This Article addresses the matters that prior academic scholarship on deepfakes obscures. It is the first to propose a new addition to the Federal Rules of Evidence reflecting a novel reallocation of fact-determining responsibilities from the jury to the judge, treating the question of deepfake authenticity as one for the court to decide as an expanded gatekeeping function under the Rules. The challenges of deepfakes—problems of proof, the “deepfake defense,” and juror skepticism—can be best addressed by amending the Rules for authenticating digital audiovisual evidence, instructing the jury on its use of that evidence, and limiting counsel’s efforts to exploit the existence of deepfakes