The Malicious Use of AI-Based Deepfake Technology as the New Threat to Psychological Security and Political Stability

Pantserev, Konstantin A.

doi:10.1007/978-3-030-35746-7_3

Cited by 42 publications

(14 citation statements)

References 5 publications

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“…Next to necessitated measures at the level of legal frameworks to protect underage victims, the subtle case of adult targets calls for instance for a civil reporting office collaborating with social media platforms which could initiate a critical dialogue with the other party to bring about an immediate deletion or at least categorical refraining from further dissemination of the material which can be calibrated to the expectations of the target. Recently, the malicious design of deepfakes has been described as a "[...] serious threat to psychological security" [167].…”

Section: Rcra (Additional Non-overlapping Guidelines)mentioning

confidence: 99%

Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

Aliman¹,

Kester²,

Yampolskiy³

2020

Preprint

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In the last years, AI safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research.

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Section: Rcra (Additional Non-overlapping Guidelines)mentioning

confidence: 99%

Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

Aliman¹,

Kester²,

Yampolskiy³

2020

Preprint

View full text Add to dashboard Cite

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“…has reduced trust in social media. Further, with the introduction of deepfakes, the synthesis of convincing, highly detailed, and novel human faces is easier to access, provoking psychological dilemmas in discriminating the truth (Pantserev 2020). Numerous research has shown the importance of the impact of content and layout of social media posts for user engagement (Shahbaznezhad, Dolan, and Rashidirad 2021).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks

Silva

Das

Aladdini

et al. 2022

ICWSM

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Advances in Artificial Intelligence (AI) have made it possible to automate human-level visual search and perception tasks on the massive sets of image data shared on social media on a daily basis. However, AI-based automated filters are highly susceptible to deliberate image attacks that can lead to content misclassification of cyberbulling, child sexual abuse material (CSAM), adult content, and deepfakes. One of the most effective methods to defend against such disturbances is adversarial training, but this comes at the cost of generalization for unseen attacks and transferability across models. In this article, we propose a robust defense against adversarial image attacks, which is model agnostic and generalizable to unseen adversaries. We begin with a baseline model, extracting the latent representations for each class and adaptively clustering the latent representations that share a semantic similarity. Next, we obtain the distributions for these clustered latent representations along with their originating images. We then learn semantic reconstruction dictionaries (SRD). We adversarially train a new model constraining the latent space representation to minimize the distance between the adversarial latent representation and the true cluster distribution. To purify the image, we decompose the input into low and high-frequency components. The high-frequency component is reconstructed based on the best SRD from the clean dataset. In order to evaluate the best SRD, we rely on the distance between the robust latent representations and semantic cluster distributions. The output is a purified image with no perturbations. Evaluations using comprehensive datasets including image benchmarks and social media images demonstrate that our proposed purification approach guards and enhances the accuracy of AI-based image filters for unlawful and harmful perturbed images considerably.

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“…Although these neutral synthesis techniques can generate high-quality images or videos to help create facial visual effects [9], they can also be abused by malicious users. Misinformation tends to spread quickly on the Internet, causing severe trust and security concerns in our society [10,11], given that the personal face implies sensitive identity information. To 1 Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection

Wang¹,

Jiang²,

Jin³

et al. 2021

Preprint

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As the remarkable development of facial manipulation technologies is accompanied by severe security concerns, face forgery detection has become a recent research hotspot. Most existing detection methods train a binary classifier under global supervision to judge real or fake. However, advanced manipulations only perform small-scale tampering, posing challenges to comprehensively capture subtle and local forgery artifacts, especially in high compression settings and cross-dataset scenarios. To address such limitations, we propose a novel framework named Multi-modal Contrastive Classification by Locally Correlated Representations (MC-LCR), for effective face forgery detection. Instead of specific appearance features, our MC-LCR aims to amplify implicit local discrepancies between authentic and forged faces from both spatial and frequency domains. Specifically, we design the shallow style representation block that measures the pairwise correlation of shallow feature maps, which encodes local style information to extract more discriminative features in the spatial domain. Moreover, we make a key observation that subtle forgery artifacts can be further exposed in the patch-wise phase and amplitude spectrum and exhibit different clues. According to the complementarity of amplitude and phase information, we develop a patch-wise amplitude and phase dual attention module to capture locally correlated inconsistencies with each other in the frequency domain. Besides the above two modules, we further introduce the collaboration of supervised contrastive loss with cross-entropy loss. It helps the network learn more discriminative and generalized representations. Through extensive experiments and comprehensive studies, we achieve state-of-the-art performance and demonstrate the robustness and generalization of our method.

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The Malicious Use of AI-Based Deepfake Technology as the New Threat to Psychological Security and Political Stability

Cited by 42 publications

References 5 publications

Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks

MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection

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